INRIA, Evaluation of Research Theme Cog B

1 Administrative aspects

Project-team acronym : VISTA

Project-team title : Spatio-temporal Vision

Scienti�c leader : Patrick Bouthemy

Research center : Rennes

Common project-team with : CNRS, University of Rennes 1

Personnel (March 2001)

Misc. INRIA CNRS University Total

DR (1) / Professors 2 1 3

CR (2) / Assistant Professors 1 2 1 4

Permanent Engineers (3) 1 1

Temporary Engineers (4) 1 1

PhD Students 4 3 1 4 12

Post-Doc. 0

Total 4 8 4 5 21

External Collaborators 0

Visitors (> 1 month) 0

(1) \Senior Research Scientist (Directeur de Recherche)"(2) \Junior Research Scientist (Charg�e de Recherche)"(3) \Civil servant (CNRS, INRIA, ...)"(4) \Associated with a contract (Ing�enieur Expert or Ing�enieur Associ�e)"

Personnel (November 2005)

Misc. INRIA CNRS University Total

DR / Professors 2 1 3

CR / Assistant Professor 1 2 1 3

Permanent Engineer 0

Temporary Engineer 2 2

PhD Students 4 4 2 10

Post-Doc. / ATER 1 1

Total 5 9 1 4 19

External Collaborators 1 1

Visitors (> 1 month) 1 1

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Changes in sta�

DR / Professors Misc. INRIA CNRS University totalCR / Assistant Professors

Arrival 1 5

Leaving 4 2

Comments : Patrick Gros (CR CNRS) left in 2002 to create his own team TEXMEX.In January 2004, two new teams were created as \spin-o�" of VISTA: LAGADIC teaminvolving Fran�cois Chaumette (Head, DR Inria), Eric Marchand (CR Inria), and VISAGESteam involving Christian Barillot (Head, DR CNRS since 10/01), Pierre Hellier (CR Inria)and Sylvain Prima (CR Inria). Let us add that Fr�ed�eric Cao will leave Vista in January2006 to join Cognitech company in Pasadena, USA (not included in the leaving Inria CRin the Table above).

Current composition of the project-team (November 2005):

� Patrick Bouthemy, Head, DR Inria

� Fr�ed�eric Cao, CR Inria (leaving in January 2006)

� Charles Kervrann, CR Inra (on Inria secondment, since July 2003)

� Ivan Laptev, CR Inria (from September 2005, formerly Post-Doc from Dec. 2004)

� Jean-Pierre Le Cadre, DR CNRS

� Etienne M�emin, MC University of Rennes 1

� Patrick P�erez, DR Inria (since Feb. 2004)

� Jian-Feng Yao, external collaborator, Prof. University of Rennes 1, IRMAR, MathsDept

� Bruno Cernuschi-Frias, visitor, Prof. University of Buenos-Aires, (Oct. to Dec.2005)

� Babu Venkatesh, Post-Doc Ercim (until March 2005)

� Brigitte Fauvet, Temporary Engineer, RIAM-Feria contract (until June 2005)

� Nicolas Gengembre, Temporary Engineer Inria, FT-RD contract

� Patrick Heas, Temporary Engineer Inria, IST-Fluid contract (from June 2005)

� Vincent Auvray, Ph-D student, Cifre grant (GE Healthcare)

� J�erome Boulanger, Ph-D student, Inra-Inria grant (ACI funding)

� Thomas Br�ehard, Ph-D student, DGA grant (thesis defense in December 2005)

� Aur�elie Bugeau, Ph-D student, Research Ministry grant (University)

� Tomas Crivelli, Ph-D student, University of Buenos-Aires (from Sept. 2005)

� Anne Cuzol, Ph-D student, Research Ministry grant (University)

� Alexandre Hervieu, Ph-D student, Inria grant (from Nov. 2005)

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� Nicolas Papadakis, Ph-D student, Inria grant (IST-FLUID contract)

� Gw�ena�elle Piriou, Ph-D student, Inria grant (Regional funding, thesis defense inDecember 2005), now ATER (temporary lecturer) University of Rennes 1

� C�ecile Simonin, Ph-D student, DGA grant (DGA-CEP Arcueil, from Oct. 2005)

� Thomas Veit, Ph-D student, Inria grant (Regional funding, thesis defense in Decem-ber 2005)

� Huguette B�echu, secretary, Inria (with Temics team)

Current position of former project-team members (including PhD stu-dents during the 2001-2005 period):

- Patrick Gros, CR CNRS, moved to Texmex team (2002)- Sid-Ahmed Berrani, Ph-D student, m.t. Texmex team- Christian Barillot, DR CNRS, m.t. Visages team (2004)- Pierre Hellier, CR Inria, m.t. Visages team- Sylvain Prima, CR Inria, m.t. Visages team- Laure Ait-Ali, Ph-D student, m.t. Visages team- Cyb�ele Ciofolo, Ph-D student, m.t. Visages team- Arnaud Ogier, Ph-D student, m.t. Visages team- Fran�cois Chaumette, DR Inria, m.t. Lagadic team (2004)- Eric Marchand, CR Inria, m.t. Lagadic team- Fabien Spindler, Permanent Engineer Inria, m.t. Lagadic team- Peihua Li, Post-Doc Inria, m.t. Lagadic team- Andrew Comport, Ph-D student, m.t. Lagadic team- Nicolas Mansard, Ph-D student, m.t. Lagadic team- Muriel Pressigout, Ph-D student, m.t. Lagadic team- Anthony Remazeilles, Ph-D student, m.t. Lagadic team- Omar Tahri, Ph-D student, m.t. Lagadic team- C�edric Riou, Temporary Engineer Inria, m.t. Lagadic team- Anne-Sophie Tranchant, Temporary Engineer Inria, m.t. Lagadic team

- Elise Arnaud (Ph-D student), Post-Doc, University of Genova, Italy- Venkatesh Babu (Post-Doc Inria), Post-Doc, Singapore- Fran�cois Coldefy (Temporary Engineer Inria), Engineer, FT-RD, Lannion- Isabelle Corouge (Ph-D student), Post-Doc, University of North Carolina, Chapel Hill,USA- Thomas Corpetti (Ph-D student), Research scientist (CR) CNRS, Costel, University ofRennes 2- Fr�ed�eric Dambreville (Ph-D student, Post-Doc Naval Research Lab, USA), Research sci-entist, DGA, CEP Arcueil- Fabien Dekeyser (Ph-D student), R&D Engineer, CEA-LETI, Saclay- Pierre Dodin (Post-Doc Inria), (Temporary position DGA-Cesta, Bordeaux), Engineer,Artrovision company, Montreal- Nicolas Ducoin (Temporary Engineer Inria), Engineer, Software company- Ronan Fablet (Ph-D student, Post-Doc Brown Univ., USA), Research scientist, Ifremer,Brest- Gr�egory Flandin (Ph-D student), R&D Engineer, Astrium company, Toulouse- Carine Hue (Ph-D student), Research scientist (CR) INRA, Toulouse

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- Cha�k Kermad (Temporary Engineer Inria)- Gildas Lefaix (Temporary Engineer Inria), Engineer, Software company- Youcef Mezouar (Ph-D student, Post-doc Columbia Univ., USA), Ass. Prof. (MC) Uni-versity of Clermont-Ferrand- S�ebastien Paris (Temporary Engineer Inria), Ass. Prof. (MC) University of Toulon- Nathalie Peyrard (Post-Doc Inria), Research scientist (CR) INRA, Avignon- Fran�cois Rousseau (Ph-D student, Post-Doc UCSF, USA) ATER (temporary lecturer)University of Strasbourg- Vincent Samson (Post-Doc Inria), R&D Engineer, Astrium company, Toulouse- Emmanuel Veneau (Ph-D student), Teacher

Last INRIA enlistments

� Fr�ed�eric Cao (Ph-D thesis: ENS Cachan, DGA) CR2, September 2001

� Pierre Hellier (Ph-D thesis: Irisa, Post-Doc: Univ. Utrecht) CR2, September 2001

� Sylvain Prima (Ph-D thesis: Inria Sophia-Antipolis, Post-Doc: Mc-Gill Univ. Mon-treal) CR2, September 2003

� Charles Kervrann, CR1 INRA, July 2003 (on Inria secondment)

� Patrick P�erez, DR2, February 2004 (after a 4-year stay in Microsoft Research, Cam-bridge, UK)

� Ivan Laptev (Ph-D thesis: KTH Stockholm) CR2, September 2005

Other comments :

The Vista team has known important changes over the last four years, since three newteams were created by people formerly involved in Vista team: Texmex team (P. Gros) in2002 concerned with multimedia indexing, Lagadic team (F. Chaumette) in 2004 devotedto visual servoing, robot vision and augmented reality, Visages team (C. Barillot) in 2004devoted to medical image computing and computer assisted interventions. It demonstratesthe dynamism of the group and the ability of the Vista team to disseminate.

2 Scienti�c aspects

2.1 Keywords

Keywords

Image sequence, dynamic scene analysis, statistical modeling, Markov models, Bayesianestimation, robust estimation, a contrario decision, particle �ltering, statistical learning,motion detection, motion segmentation, optic ow, motion recognition, uid motion analy-sis, registration, tracking, trajectography, video processing, video indexing, meteorologicalimagery, experimental uid mechanics, biological imagery.

2.2 Research �elds

Vista research work is concerned with various types of spatio-temporal images, (mainlyvideo images, but also meteorological satellite images, video-microscopy, x-ray images).We are investigating methods to analyze dynamic scenes, and, more generally, dynamicphenomena, within image sequences. Application-wise, we focus our attention on three

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main domains: content-aware video applications, meteorological imaging and experimentalvisualization in uid mechanics, biological imaging, while a related fourth domain (detec-tion and surveillance for defense applications) is also considered. For that, a number ofcollaborations, academic and industrial, national and international, are set up.

2.3 Overall objectives

We address the full range of problems raised by the analysis of dynamic image contentswith a focus on image motion analysis issues: image sequence denoising, motion detection,motion estimation, motion-based segmentation, tracking, motion recognition and interpre-tation with learning. We usually rely on statistical approaches, resorting to: Markov mod-els, Bayesian inference, robust estimation, a contrario decision, particle �ltering, learning.We aim at both designing important academic contributions in these �elds and handlingimportant issues in the above mentioned application domains to develop well-founded,useful and e�cient solutions. Regarding motion detection, we want to de�ne principled,parameter-free general methods able to provide detection con�dence rates. To this end,the a contrario decision framework is investigated. In order to handle complex video con-tents as exhibited by broadcast TV programs or amateur videos, we are elaborating new(mixed-state) probabilistic motion models along with the associated learning and recog-nition methods. To de�ne e�cient and reliable tracking methods in the same context, wehave to cope with multiple moving objects, occlusions, appearance changes, which are stillchallenging open issues. Di�erent non-linear tracking schemes are thus explored. Anothermain objective is the design of a complete framework for uid motion analysis, includingthe estimation of dense velocity �elds, their parametric representation and structuration,and the tracking of the uid ow structures of interest (e.g., vortices). As a consequence,physical (static and dynamic) models need to be incorporated in a tractable and rele-vant way in the corresponding image processing methods. In biological imaging, we aredeveloping novel methods for processing 3D image sequences, including spatio-temporaldenoising, small moving object detection and tracking. These new tools should allow inno-vative investigations in biology and life science, especially to demonstrate concepts relatedto molecular dynamics.

2.4 Scienti�c foundations

We hereafter outline the four main aspects of the scienti�c foundations which we arerelying on for our research work. Mathematical details are given in our activity reports(year 2004 or year 2005).

2.4.1 Motion estimation and motion segmentation with mrf models

Assumptions (i.e., data models) must be formulated to relate the observed image intensitiesto motion, and other constraints (i.e., motion models) must be added to solve problemslike motion segmentation, optical ow computation, or motion recognition. The motionmodels are supposed to capture known, expected or learned properties of the motion�eld ; this implies to somehow introduce spatial coherence or more generally contextualinformation. The latter can be formalized in a probabilistic way with local conditionaldensities as in Markov models. It can also rely on prede�ned spatial supports (e.g., blocksor pre-segmented regions). The classic mathematical expressions associated with the visualmotion information are of two types. Some are continuous variables to represent velocityvectors or parametric motion models. The others are discrete variables or symbolic labels

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to code motion detection (binary labels), motion segmentation (numbers of the motionregions or layers) or motion recognition output (motion class labels).

2.4.2 Fluid motion analysis

Analyzing uid motion is essential in a number of domains and can rarely be handledusing generic computer vision techniques. In this particular application context, we studyseveral distinct problems. We �rst focus on the estimation of dense velocity maps fromimage sequences. Fluid ows velocities cannot be represented by a single parametric modeland must generally be described by accurate dense velocity �elds in order to recover theimportant ow structures at di�erent scales. Nevertheless, in contrast to standard motionestimation approach, adapted data model and higher order regularization are requiredin order to incorporate suitable physical constraints. In a second step, analysing suchvelocity �elds is also a source of concern. When one wants to detect particular events, tosegment meaningful areas, or to track characteristic structures, dedicated methods mustbe devised and studied.

2.4.3 Object tracking with non-linear probabilistic �ltering

Tracking problems that arise in target motion analysis (tma) and video analysis are high-ly non-linear and multi-modal, which precludes the use of Kalman �lter and its classicvariants. A powerful way to address this class of di�cult �ltering problems has becomeincreasingly successful in the last ten years. It relies on sequential Monte Carlo (smc) ap-proximations and on importance sampling. The resulting sample-based �lters, also calledparticle �lters, can, in theory, accommodate any kind of dynamical models and observationmodels, and permit an e�cient tracking even in high dimensional state spaces. In practice,there is however a number of issues to address when it comes to di�cult tracking prob-lems such as long-term visual tracking under drastic appearance changes, or multi-objecttracking.

2.4.4 A contrario decision approach for motion analysis

We have recently been interested in automatic detection problems in image sequence pro-cessing. A fundamental question is to know whether it is possible to automatically directthe attention to some object of interest (in the broad sense). We have been using a gener-al grouping principle, asserting that conspicuous events are those that have a very smallprobability of occurence in a random situation. We have applied this principle formalizedwithin an a contrario decision framework to the detection of moving objects in an imagesequence, to image comparison, and to the matching of shapes in images.

2.5 Application domains

We are dealing with the following application domains (mainly in collaboration with thelisted partners) :

� Content-aware video applications (INA, Thomson, FT-RD, PSA);

� Experimental uid mechanics (Cemagref) and meterological imagery (lmd). We arealso leading the IST-FET European project FLUID and are involved in the Inriaassociate team FIM with the University of Buenos-Aires;

� Biological imagery (Inra, Curie Institute, Biology Dpt of University of Rennes 1)

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� Surveillance (Onera, Thales, collaborations are nevertheless considered only froman academic viewpoint). The main addressed issues are search and surveillance,navigation, distributed tracking with a sensor network.

2.5.1 Content-aware video applications

The amount of video footage is constantly increasing due to the dissemination of videocameras, the broadcasting of TV programs by multiple means, the seamless acquisitionof personal videos,. . . The exploitation of video material, whatever its usage, requires au-tomatic (or at least semi-automatic) tools to process video contents. A wide range ofapplications can be envisaged dealing with editing, analyzing, annotating, browsing andauthoring video contents. Video indexing and retrieval for audio-visual archives is, forinstance, a major application, which is receiving lots of attention. Other needs includethe creation of enriched videos, the design of interactive video systems, the generationof video summaries, and the development of re-purposing frameworks (speci�cally, for 3Gmobile phones and Web applications). For most of these applications, tools for segmentingvideos, detecting events or recognizing actions are usually required.

We are interested in the processing of videos which are shot (and broadcast) in theaudiovisual domain, more speci�cally, sports videos but also dance videos or TV shows.Amateur videos of similar content can also be within our concern. On one hand, sportsvideos raise di�cult issues, since the acquisition process is weakly controlled and contentexhibits high complexity, diversity and variability. On the other hand, motion is tightlyrelated to sports semantics. Besides, the exploitation of sports videos forms an obviousbusiness target. We have developed several methods and tools in that context address-ing issues such as shot change detection, camera motion estimation and characterization,object tracking, motion modeling and recognition, event detection, video summarization.Beside this main domain of applications, we are also investigating gesture analysis prob-lems. An on-going project in particular aims at monitoring automatically car drivers'attention.

2.5.2 Experimental uid mechanics and meterological imagery

Concerning the analysis of uid ows from image sequences, we mainly focus on the do-mains of experimental uid mechanics and meteorological imaging. We have to face ahuge amount of high resolution image sequences which reveal the space-time evolution of ow structures in a non intrusive way. The types of images involved in these applicationdomains may vary, depending on the experimental imaging set-up and/or the image sen-sor used. Very speci�c applications may be tackled for some type of images, but generaland common goals can nevertheless be de�ned in terms of motion analysis. Image mo-tion estimation aims at providing instantaneous measurements of the ow velocity, whichcan help physicists to analyze complex uid ows. In both domains, the estimation ofvelocity ow �elds from an image sequence is routinely performed with local methodswhich rely on the computation of average displacements by cross-correlation over smallsearch windows. Although sophisticated block-matching schemes have been designed inorder to cope with intrinsic di�culties of particle-seeded images or atmospheric satelliteimages, these approaches can hardly cope with low contrast visualization techniques suchas Schlieren images or images of the MSG (Meteosat Second Generation satellite) watervapor channel. These methods are not convenient either to get dense velocity �elds thatare accurate enough at di�erent scales for highly non constant motion, so that relevant ow features can be reliably extracted. Besides, the incorporation of uid ow dynamiclaws (almost inescapable in a near future with image sequences of higher time resolution)

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cannot be handled with local correlation methods. As a matter of fact, no spatial and tem-poral coherency can be handled with such processing techniques as they operate entirelyin a data-driven way allowing no incorporation of physical prior knowledge (related to thebasic equations of uid mechanics). From that point of view, motion analysis techniquesdeveloped in computer vision are particularly appealing as they combine model-drivenvariational smoothness functions with data-driven terms.

On such a basis, our �rst objective in the meteorological domain consists in designingtechniques for an accurate estimation of the atmospheric wind �elds. Such a goal shouldrequire �ne sophisticated schemes incorporating physical models of the atmosphere. Thesecond goal is to propose methods for tracking cloud systems of importance. This is usefulfor the surveillance of potentially dangerous meteorological phenomena such as convectiveclouds, hurricane, tornadoes, etc. These developements should have a signi�cative impacton weather forecasting, risk prevention, or enhancement of global atmospheric circulationmodel assimilation.

As for experimental uid mechanics, we are designing new methods for the analysis ofcomplex uid ows from image sequences. A large range of applications is concerned for in-stance with turbulent ows in aerodynamics, aeronautics, heat transfer, etc. Applicationsinvolving ow control are of particular interest ( ow separation delay, mixing enhancemen-t, drag reduction,...). These applications need enhanced visualization and sound numericaltechniques such as low-order modeling with reduced dynamical models. The processing ofreal data and the accuracy enhancement of spatio-temporal measurements may togetherbring improvements in the modeling of turbulent ows which is traditionally based solelyon initial conditions captured through experimental measures.

2.5.3 Biological imagery

Recent progresses in molecular biology and light microscopy make henceforth possiblethe acquisition of multidimensional data (3D+time) at one or several wavelengths (multi-spectral imaging) and the observation of intracellular molecular dynamics at sub-micronresolutions in biology. Automatic image processing methods to study molecular dynamicsfrom image sequences are therefore of major interest, for instance, for membrane tra�ck-ing involving the movement of small particles from donor to acceptor compartments withinthe living cell. The challenge is then to track GFP tags ( uorescent proteins for labeling)with high precision in movies representing several gigabytes of image data. The data haveto be collected and processed automatically to generate information on partial or com-plete trajectories. In our research work, we develop methods to perform the computationalanalysis of these complex 3D image sequences since the capabilities of most commercialimage analysis tools for automatically extracting information are rather limited and/orrequire a large amount of manual interactions with the user.

2.6 Main contributions

We hereafter describe our main research contributions over the last four years. They areorganized in �ve main items.

2.6.1 A contrario methods for motion detection and matching

We have investigated a novel approach for images and video sequences analysis. The pointof view is that, for low level tasks, no prior learning is necessary. On the contrary, it is pos-sible to devise methods allowing us to detect conspicuous events in a very robust way. The

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basic idea of this theory was given by Helmholtz who claimed that something is conspicu-ous when it has a very low probability to occur by chance. In other terms, only structureis visible, and chance does not generate structure. If we can detect large deviation fromchance, then we can detect structure. A mathematical and computational theory startingfrom this principle was initiated a few years ago, at �Ecole Normale Sup�erieure de Cachan.Since then, Vista has become one of the main contributors to this novel approach, ondi�erent topics.

a) Image and shape comparison.

The purpose is to answer the following question: \given two images, do they have partsin common". Since there are no assumptions on the content of the images, no prior learn-ing can be used. Thus a general comparison criterion is needed. This criterion has tobe geometrically invariant, robust and automatic. Strangely enough, this decision step isoften neglected in the literature, where authors usually focus on the de�nition of local andinvariant descriptors. We proved that decision could be made in a more sensible way than�xing arbitrary and ad hoc thresholds. Possible applications are of course image retrievaland indexing in video streams and in image or video databases.

b) Motion detection (both with static and mobile camera).This is a necessary �rst step in many applications, though not a solved problem. Onone hand, there are fast local methods giving pointwise (or close to pointwise) decisions.On the other hand, there are methods based on some regularization hypothesis or priorknowledge. Again, we develop a low-level method aiming at taking decision automatically.It consists in proving that some measures of motion cannot be independently distributedin some regions of the image. As a consequence, they must belong to a group, which islikely to correspond to a part of a moving object in the scene.

c) Track initialization, trajectories detection.

There is a gap between change detection methods and tracking algorithms. The latterusually have more elaborated models of dynamics, but assume that the object has beenpreviously detected (sometimes done by hand). Following some works on the analysis ofvisual motion, we detect trajectories when parts of the sequence may be described bysimilar simple parametric models. Interestingly, the detection may be formalized as aclustering problem in the space of motion parameters. We devised an a contrario methodfor such a grouping procedure.

2.6.2 Mixed-state probabilistic models for motion modeling and recognition

a) Mixed-state probabilistic motion models

In di�erent contexts and speci�cally in motion analysis, information of interest can berepresented by both discrete and continuous values. Let us mention the case of velocity�elds including velocity vectors and motion discontinuities. Typically, if we compute localmotion magnitudes, the 0 value indicates the absence of motion which is a symbolic in-formation of key interest. Consequently, we have introduced motion variables which cantake both discrete and continuous values, called mixed-state variables. The correspondingmixed-state probabilistic motion models are formed by the mixture of the Dirac densityat 0 and a density from the exponential family for the continuous part (0 value excluded).They o�er a low-dimensional and accurate representation of histograms of local motionmeasurements (occurence statistics). We have exploited these novel models for motionlearning from video segments, motion recognition and event detection in videos. We have

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also de�ned a temporal causal mixed-state model.

b) Mixed-state auto-models

Markov random �elds (MRF) models are now a standard tool in image analysis. However,the existing models deal either with continuous observations, or with discrete observations.Indeed, discrete (or symbolic) information is usually handled with a label process. Howev-er, the latter is a latent process and the resulting statistical inference methods need in gen-eral its restoration (i.e., segmentation). This classical approach is possible only upon thecost of a generally huge computation e�ort. We have proposed a quite di�erent approach.The aim is to give a model which automatically deals with the two types of observations,without the introduction (and the inference) of any latent process. We have followed theconstruction of auto-models by J. Besag, and extended it to the multi-parameter case,while introducing necessary adaptations for mixed-states variables. We have developed anestimation procedure of the model parameters based on the pseudo-likelihood criterion.Preliminary results are already obtained on the modeling of motion textures correspond-ing to videos depicting natural dynamic scenes such as rivers, sea-waves, moving foliage,�re, smokes.

2.6.3 New models and methods for uid motion estimation and structuration

We have designed original methods for the estimation and the analysis of uid ows fromimage sequences.

a) Estimation of dense velocity �elds for uid ows

We have �rst de�ned accurate dedicated methods for the estimation of uid ow velocity�elds. They incorporate prior knowledge stemming from the basic equations of uid me-chanics. They rely on speci�c data model issued from the continuity equation associatedto sound regularization priors. Such priors are either de�ned as second-order div-curlsmoothing function in the case of dense �eld estimation or as adapted basis functionsstemming from the discretization of vorticity and divergence maps for tracking purpose.Such a tracking is stated in a stochastic �ltering framework. It associates a stochasticformulation of the Navier-Stokes vorticity-velocity equation and a measurement likelihoodbased on an image reconstruction error. A deterministic approach formalized within avariational assimilation framework has been also recently investigated for the same pur-pose. The performance of our uid motion dense estimation method has been carefullyassessed on two typical experimental uid ows: a plane mixing layer and a near wakebehind a circular cylinder. This evaluation has been conducted with the Experimental uid mechanics group of Cemagref Rennes.

b) Fluid ow structuration and characterization

We have also addressed the problem of uid motion �eld analysis and characterization.This analysis has been conducted through the estimation of the potential functions asso-ciated to the irrotational and solenoidal components of the motion �elds. Such a schemeallows us to extract singular points and their associated in uence domains. This methodprovides a compact relevant representation of a uid motion in terms of source/sink andvortices.

2.6.4 Adaptive statistical methods for image sequence denoising

We have de�ned a spatio-temporal method for signi�cantly increasing the signal-to-noiseratio in noisy uorescence microscopic image sequences where small particles have to be

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tracked from frame to frame. Image sequence denoising is achieved, while preservingspace-time image discontinuities, using a spatio-temporal adaptive estimation window. Itis embedded in a statistical approach based on a bias-variance criterion. The estimationmethod is entirely data-driven and involves a (weighted) selection of data in the variable,on-line adapted, neighborhood of each pixel (two criteria have been designed, a point-wise one and a space-time examplar-based one). We have applied this method to noisysynthetic and real 4D images where a large number of small uorescently labeled vesiclesare moving in regions close to the Golgi apparatus. The SNR is shown to be drasticallyimproved and the enhanced vesicles can be segmented. It is worth noting that the methodrequires no motion estimation. Moreover, our method outperforms the best publishedimage sequence denoising methods (experimental comparison on video image sequences).

2.6.5 Non-linear tracking methods

All the tracking problems which we focus on are formalized as non-linear recursive Bayesian�ltering. Within this powerful framework, we have studied various sub-problems and mod-eling tools.

a) Robust tracking in low-dimensional state space with multi-modal likelihood

A large range of visual tracking and target motion analysis problems fall in this class.They have in particular been the main bene�ciaries in the past of particle �lters studies.There remain however a number of generic problems when using sequential Monte Carlo(SMC) techniques in this type of set-ups. The ones we have been, and still are, workingon are the following issues.- Multi-object tracking: we have designed and compared novel SMC techniques, basedeither on Gibbs sampling or sequential importance sampling to handle association uncer-tainty. We have also addressed the problem of varying number of targets by using binary\existence" variables and treating this augmented model in a way that impacts as littleas possible on existing tracking algorithms, so that existing software can be reused.- Bayesian tracking with auxiliary variables: we considered the general context of sequen-tial estimation problems with an unknown auxiliary discrete Markovian process. Theresulting approach facilitates easy re-use of existing tracking algorithms. In particularparticle �lters can be obtained based on sampling only in the original state space insteadof sampling in the augmented space, as it is usually done. This framework facilitates forinstance handling of occlusion problem.- Robust visual tracking without prior: to track arbitrary entities along videos of arbi-trary type, we investigate simple multi-cue appearance models that can be instantiated,and possibly updated, online. In particular, we are currently experimenting with combina-tions of standard complementary appearance models (intensity template instantaneouslyrefreshed and global color histogram updated very slowly).

b) High-dimensional state spaces and detailed dynamicsReliable non-linear tracking of large dimensional structures (e.g., within range 20 to 100dimensions) is very di�cult as sampling in high dimensional spaces is usually very inef-�cient. We propose to tackle this problem by using highly informative evolution modelseither estimated on-line, using motion estimation techniques or based on precise physicallaws when context permits it. Such detailed dynamics allow us to rely on simple observa-tion models (mixtures of Gaussians), which makes optimal proposal density accessible.

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c) Target tracking for partially observed nonlinear systems

In this domain, our contributions are three-fold. The common denominator is nonlinear�ltering for partially observed systems. First, developing reliable methods for trackinginitialization is a major requirement and corresponds to long-term involvement. An in-strumental idea consists in limiting tracking to the observable part of the state vector,itself initialized via MCMC methods. For the unobservable ones, uncertainty is just prop-agated. The whole is immersed in a Bayesian hierarchical �ltering setup and yields a fastand reliable algorithm. Additionally, it has been shown that a maneuverability factor (the�=r ratio) can be estimated, even for hard-stressing scenarios. Performance analysis forrandom state �ltering is another issue. Our main result is the derivation of closed formsfor the Posterior Cram�er-Rao Bound (PCRB). This is an extension of Riccati equation toa special case of non-linear and partially observed systems. Such a result is important,particularly for planning active measurement scheduling. Distributed target tracking isanother important area. This means that tracking is made on each receiver and then tracksare transmitted to a centralizer, itself performing global tracking from these elementarytracks. While this subject has received considerable attention in a linear context, this isno longer true in our nonlinear, partially observed context. Two architectures were con-sidered, with and without feedback, as well as complete or partial tracking at the receiverlevel. It is probably in the area of robustness (reject of falsely associated data, changingenvironment) and association (track-to-track) that bene�ts can be the more important.

2.7 Project-team positioning

First, we comment our positioning with respect to other INRIA project-teams which areconducting research work close to part of ours.

Movi and Lear teams: Christian Sminchisescu and Bill Triggs (Movi team, then Lear teamfor B. Triggs) have proposed several particle �ltering techniques that analyze andexploit likelihood landscapes to build better proposal densities. Although these tech-niques have been devised and tested in the speci�c context of human body trackingwith 3D articulated models (hence associated to high-dimensional structured state s-pace), they are generic. As such, they constitute particle �ltering variants that couldbe used in the context of visual tracking problems we are interested in. Apart fromthis body of work, the research activity of Lear team focuses primarily on objectrecognition and categorization in still images. As explained in Section 4, trainingsuch detectors and classi�ers can bene�t from generic tracking tools, including theones we are developing. Conversely, video analysis and understanding includes ob-ject recognition and classi�cation as one of the main tools. In the perspective ofjoint object/action recognition, as described in Section 4, the strong expertise thatLear team is developing on visual object recognition would de�nitely be helpful. Onthe other hand, the current work carried out in Movi team on motion capture ismore concerned with 3D motion and structure from multiple cameras, while we aremostly interested in 2D motion estimation and tracking in video image sequences.

Ariana team: Ariana is involved as well in statistical approaches for image analysis,but they are focused on stochastic geometry. They are also developing importantresearch on variational methods for static image processing (image decomposition,restoration, segmentation). As for applications, they are mainly investigating remotesensing problems.

Imedia team: Imedia is involved as well in multimedia indexing, but with a focus onstatic image databases and contributions on classi�cation, learning and relevance

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feedback issues.

We now describe in general terms how we position our research work, i.e., researchtopics and followed approaches, in the context of national and international research.

The work of Vista project-team on non-linear probabilistic tracking presents a fewspeci�cities compared to world-wide competition. First, we conduct detailed methodolog-ical studies on classes of generic problems of broad applicative interest. Even if the startingpoint might be a speci�c tracking application, we try to abstract the context and focuson the underlying structure of interest, hence designing techniques that go beyond vanillaones while not being ad-hoc solutions to a single problem. As a consequence of this typeof approach to tracking problems, and also because of the presence in Vista project-teamof researchers from both communities, a number of our tracking studies apply to bothvisual tracking problems and trajectography (e.g., bearings-only tracking). It is especiallytrue for all the multiple-target tracking studies we have conducted in the past four years.Finally, as regards the SMC techniques we have developed in the past four years, we havedevoted lots of attention to designing powerful (sometimes optimal) proposal densities,which rely either on a detailed analysis of current data or on strong physical priors. Thisis worth noting since most of particle �lters currently proposed in the literature are stillbootstrap �lters that take rather non-informative dynamics as proposal densities. Amongcompetitors, let us mention the closest groups: Georgia Tech (F. Delleart), EPFL (P. Fua),ETH Zurich (L. van Gool) and Brown University (M. Black) in the computer vision com-munity, and DSTO Adelaide (B . Ristic, N. Gordon) and Qinetiq, UK (S. Maskell) in thetarget motion analysis community.

We believe that we were the �rst group to launch research work on motion analysisbased on the a contrario decision framework which was introduced for image and shapeanalysis by J.-M. Morel's group at ENS Cachan. A similar problem has been recently in-vestigated in Ceremade Lab (F. Dibos). As for mixed-state auto-models, it is a completelynew modeling scheme.

Fluid image analysis has been in the past mainly investigated by uid mechanicsscientists for experimental visualization purposes. Tools routinely used in this contextrely on ad-hoc local correlation-based techniques which, we believe, can hardly incorporate uid kinematics or dynamics prior. Despite a large range of potential applications rangingfrom environmental sciences to experimental uid mechanics, only very few image analysisor computer vision groups are developing a sustained e�ort on the subject. The Vistagroup has been, to our knowledge, the �rst computer vision group to conduct a long-terminternationally renown research project in that domain.

Image sequence analysis in video-microscopy for life sciences has not been investigatedin the past by computer vision groups, but is now gaining importance because of the deepimpact of molecular biology on medicine research. The image processing techniques thatare currently used for modeling intracellular dynamics, however, are still relatively crude ifone compares them with the state-of-the art in medical imaging. In France, signi�cant andrecent research work has been done at Pasteur Institute (LAIQ team, J.-C. Olivo-Marin,P. Bouthemy was an external reviewer of A. Genovesio's Ph-D thesis), Curie Institute(\Subcellular structure and cellular dynamics" team), ENS Paris (Kastler Brossel lab)and also University of Reims (LERI lab). At the international level, other competitorsare the Biomedical Imaging Group of EPFL (M. Unser, they have developed algorithmsfor tracking a single uorescent particle in dynamic image sequences obtained by confocalmicroscopy); the Intelligent Bioinformatics Systems group in Heidelberg (methods usinggeometrical and dynamical models for the interpretation of complex data generated byanalytic processes in molecular genetics and cell biology).In contrast to the above mentioned works and others (e.g., G. Danuser - Laboratory for

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Computational Cell Biology, The Scripps Research Institute, La Jolla, CA), we proposegeneral statistical methods for image sequence denoising, space-time detection and track-ing for processing very challenging image sequences. In addition, to our knowledge, we arethe �rst computer vision group to develop image processing methods for analyzing Rabproteins involved in intracellular tra�cking.

2.8 Publications

01 02 03 04 05

PhD Thesis 6 2 3 1 (+1�) 3

H.D.R (*) 2 1 (+1�)

Journal 14 16 10 6 16

Conference proceedings (**) 27 20 22 16 24

Book chapter 1 2 3 2

Book (written) 1 1

Book (edited)

Technical report 4 7 3 4 6

(*) HDR Habilitation �a diriger des Recherches(**) Conference with a program committee (only the papers in the international

conferences and workshops are counted)(�) Work done in Vista project, but the thesis defense occurred in 2004 once Lagadicteam was created (respectively, O. Tahri's Ph-D thesis, and E. Marchand HDR thesis).

Indicate �ve main journals in which scienti�c sta� members publish their results:

1. IEEE Trans. on Image Processing

2. IEEE Trans. on Aerospace and Electronic Systems

3. International Journal of Computer Vision

4. IEEE Trans. on Pattern Analysis and Machine Intelligence

5. Journal of Mathematical Imaging and Vision

Indicate a maximum of �ve principal conferences where scienti�c sta� members pub-lished their results on a regular basis:

1. ECCV

2. CVPR

3. ICCV

4. ICIP

5. Fusion

2.9 Software

2.9.1 Advanced software

Motion2d software - parametric motion model estimation

Motion2d is a multi-platform object-oriented library to estimate 2d parametric mo-tion models in an image sequence. It can handle several types of motion models,

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namely, constant (translation), a�ne, and quadratic models. Moreover, it includesthe possibility of accounting for a global variation of illumination. The use of suchmotion models has been proven adequate and e�cient for solving problems suchas optic ow computation, motion segmentation, detection of independent movingobjects, object tracking, or camera motion estimation, and in numerous applica-tion domains, such as dynamic scene analysis, video surveillance, visual servoing forrobots, video coding, or video indexing. Motion2d is an extended and optimizedimplementation of the robust, multi-resolution and incremental estimation method(exploiting only the spatio-temporal derivatives of the image intensity function) wede�ned several years ago (Odobez-Bouthemy, JVCIR, Dec. 95). Real-time processingis achievable for motion models involving up to 6 parameters (for 256x256 images).Motion2d can be applied to the entire image or to any pre-de�ned window or regionin the image.Motion2d is released in two versions : i) Motion2d Free Edition is the version ofMotion2d available for development of Free and Open Source software only (no com-mercial use). It is provided free of charge under the terms of the q Public License. Itincludes the source code and make�les for Linux, Solaris, SunOS, and Irix. The latestversion (last release 1.3.11, January 2005) is available for download; ii) Motion2dProfessional Edition provided for commercial software development. This versionalso supports Windows 95/98 and nt. More information on Motion2d can be foundat http://www.irisa.fr/vista/Motion2D and the software can be donwloaded at thesame Web address. About 550 downloads have been registered since August 2003from all over the world, both from academic labs and companies (non identi�ed orduplicated downloads are discarded in this total number).

Dense-Motion software - optical ow computation

The Dense-Motion software written in c enables to compute a dense velocity �eldbetween two consecutive frames of a sequence. It is based on an incremental ro-bust method encapsulated within an energy modeling framework. The associatedminimization is based on a multi-resolution and multigrid scheme. The energy iscomposed of a data term and a regularization term. The user can choose amongtwo di�erent data models : a robust optical ow constraint or a data model basedon an integration of the continuity equation. Two models of regularization canbe selected as well : a robust �rst-order regularization or a second-order Div-Curlregularization. The association of the latter with the data model based on the con-tinuity equation constitutes a dense motion estimator dedicated to image sequencesinvolving uid ows. It was proven to supply very accurate motion �elds on variouskinds of sequences in the meteorological domain or in the �eld of experimental uidmechanics.

Two other softwares have also been distributed and used by other academic groups orindustrial R&D groups: D-change software (detection of moving objects for a staticcamera), MD-Shots (detection of shot changes in video).

2.9.2 Prototype software

All the methods we have designed in our research or applied works have their counterpartsas implemented software.

VTrack software - generic interactive visual tracking platform

We have started to develop an interactive tracking platform (Windows Visual c++

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development with Microsoft mfc and Intel Opencv). It already includes a num-ber of state-of-the-art generic tracking methods (template matching, kernel-basedtracking with global color characterization, particle �ltering) as well as a numberof visualization features for enhanced experimental and demonstration experiences.The exible architecture and the rich hci allow easy design, implementation andtest of novel trackers.

2.10 Collaborations

2.10.1 Collaborations with other INRIA project-teams:

Texmex and Metiss teams: We have a long-term collaboration with these two teamson multimedia indexing and more speci�cally on cross-modal video indexing andsummarization (image, sound and speech, text). We are developing a common mul-timedia indexing platform at Irisa. We have been partners (all three or with oneof them) on several national R&D projects (Domus Videum, Feria, Mediaworks).Co-signed publications.

Visages team: collaboration on the use of local registration method applied to the esti-mation of multiple sclerosis lesion growing (co-signed publication).

Clime and Idopt teams: this collaboration takes place within the Assimage project sup-ported by the French programme ACI \Masse de donn�ees". It is concerned with theexploitation of image data for asssimilation techniques in the context of geophysical uid ows.

Aspi team: We have been involved in the CNRS speci�c action, \Particle �ltering meth-ods", led by F. Le Gland. This action was concerned with the tracking of movingentities in sonar/radar or in videos. Several workshops have been organized forfederating research on particle �ltering at the national level.

Imedia and Ariana teams: All three teams (Ariana, Imedia and Vista) are involved in theEuropean Network of Excellence MUSCLE. Imedia and Vista teams were partnersof the Mediaworks project supported by the French PRIAMM programme.

Lear team: Both teams (Lear and Vista) were involved in the European IST projectLava.

Odyss�ee team: A focused collaboration on optic ow estimation (co-signed publication).

Temics team: Both teams were involved in the RNTL Domus Videum project.

2.10.2 Collaborations with French research groups outside INRIA:

CMLA, ENS Cachan and ENST Paris: Long-term collaboration with J.-M. Morel's groupat ENS Cachan and with Yann Gousseau at ENST Paris, on a theory of shape recog-nition based an a contrario approach. Co-supervision by F. Cao of the Ph-D thesesof Pablo Mus�e and Fr�ed�eric Sur. Co-signed publications. A book in preparation.

Cemagref (\Aerolique and Biocontamination" group, G. Arroyo): long-term collabora-tion in experimental uid mechanics and image processing. We can point out thethorough validation of our optical- ow estimator on experimental uid ows (co-signed publications). This collaboration is also active within the European FLUIDproject and the ACI project called Assimage.

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IRIT Toulouse (Multimedia indexing group, R. Andr�e-Obrecht and P. Joly): collabora-tion on cross-modal video segmentation and indexing within the Feria project.

IRCCYN Nantes and LABRI Bordeaux (D. Barba and J. Benois-Pineau's groups): col-laboration on video structuration and summarization within the Domus Videumproject.

Compi�egne University of Technology (UTC, Heudiasyc lab): collaboration in the contextof the ACI Behaviour project (see 2.14). It concerns detection, tracking and motionanalysis of car drivers' parts as seen by di�erent on-board cameras.

Onera: We have frequent collaborations with ONERA (DTIM), especially in the �elds ofnavigation and target tracking. A common denominator to these studies is particle�ltering and performance assessment.

Partnership in meteorological applications:

� Costel Lab, University of Rennes 2: At the moment, we have an informalcollaboration on motion analysis for Meteosat satellite images.

� LMD (\Laboratoire de M�et�eorologie Dynamique", CNRS-X-ENS, Paris): wecollaborated with the LMD within the EUMETSAT ITT contract for the studyof alternative methods for wind �elds estimation. The LMD is also partner ofthe European FLUID project.

Partnership in biological imaging:

� INRA Applied Mathematics and Informatics Unit - Jouy en Josas: Tracking of uorescent molecules in video-microscopy (ACI IMPBio)

� INRA Mathematics, Informatics and Genome Unit - Jouy en Josas: Detectionof rare events in images.

� Curie Institute: UMR 144 - CNRS (\Compartimentation et Dynamique Cel-lulaires" Laboratory) - Paris: Methods for tracking speci�c Rab6a and Rab6a'proteins involved in the regulation of transport from the Golgi apparatus to theendoplasmic reticulum (ACI IMPBio).

� University of Rennes 1: New image analysis methods are developed for track-ing uorescent molecules (Quantum dots) linked to microtubules (AC DRAB).Collaborations with teams of the Biology Department and the Chemistry De-partment: UMR 6026 (\Interactions Cellulaires et Mol�eculaires" Laboratory -\Structure et Dynamique des Macromol�ecules" team); UMR 6510 (\Synth�eseet �Electrosynth�ese Organiques" Laboratory - \Photonique Mol�eculaire" team).

2.10.3 Collaboration with Foreign research groups:

University of Mannheim (CVGPR group, C. Schn�orr): this active collaboration startedseveral years ago (in 2001) supported by the French-German collaborative program.Several mutual visits and stays of Ph-D students. Co-signed publications. Now,partner of the European FLUID project. In this context, we collaborate on uidmotion estimation issues.

University of Buenos-Aires (Engineering Faculty, Signal Processing group, B. Cernuschi-Frias, and Fluid Mechanics group, G. Artana): Long-term cooperation with mutualvisits and long stays of Ph-D students. It was initialized by the sabbatical visit of

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B. Cernusch-Frias in 2001-2002. From Dec. 2004, it has been supported by Inriathrough the creation of the FIM Inria associate team. We collaborate on trackingand motion analysis for uid motion in image sequences, and on dynamic textureanalysis in videos of natural scenes.

University of Las Palmas, Gran Canaria (L. Alvarez): partner of the European FLUIDproject. Our collaboration is focusing on 3D motion estimation of uid phenomena.Mutual visits before the starting of this project.

University of Cambridge, U.K., Eng. Dpt, Signal Proc. Group (J. Vermaak): two-year collaboration funded by a Royal Society/CNRS collaborative programs. It ismainly concerned with multi-target tracking with probabilistic tools, both in videoprocessing and in signal processing.

University of California, San Diego (UCSD), USA. Collaboration with S.J. Belongie onthe problem of detecting and segmenting periodic activities in complex video se-quences (co-signed publications).

Royal Institute of Technology (KTH), Stockholm Sweden. Collaboration with B. Caputoon human action recognition in video sequences using local motion descriptors andSVM (co-signed papers).

Sup'Com Tunis (2001). Video indexing.

University of Utrecht and University of Cambridge (2001-2003). Collaboration on theprocessing of ultrasound image sequences conducted by C. Barillot and P. Hellier(now with Visages team).

Mc Gill University: long term collaboration on brain image processing (2001-2003, nowwith Visages team, C. Barillot).

University of Western Ontario at London, Canada (2003). Collaboration on 3D ultra-sound imaging. C. Barillot (now with Visages team) did a fourth-month stay at theUniversity of Western Ontario in 2003.

IST Lisbonne: long-term collaboration on visual servoing and robot vision (2001-2003,now with Lagadic team, F. Chaumette).

2.11 Speci�c hardware for experimental purpose (if relevant):

2.12 Speci�c software for experimental purpose (if relevant):

2.13 Industrial collaborations:

FT-RD (France-Telecom) contract: Probabilistic visual tracking for annotation of teamsport broadcasts Duration: 18 months, started in Dec. 2004. Budget: 150 kEuros.Goal: design of probabilistic tracking tools to help operators annotate televisionbroadcasts of teams sport. We are �rst trying to improve state-of-the-art techniquesfor generic tracking of a single arbitrary object, which are not su�cient in thiscontext. Extension to joint multi-player tracking and to application-speci�c prior

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incorporation (e.g., learning of player and �eld detectors) will be considered in thesecond part of the contract.

Ifremer: Two contracts, 6 months (March-August) 2003 and 5 months (April-August)2004. Budget: 24 kEuros. Analysis of �sh otolith images, detection of parallelism,detection of nuclei, interpolation of direction �elds. The latter project is continuedas a collaboration with the supervision of Anatole Chessel's PhD thesis.

General Electric Healthcare: Duration 36 months. Budget 30 kEuros. This contract isassociated to the CIFRE grant of V. Auvray's Ph-D thesis on transparent motionestimation and temporal denoising of uoroscopic cardiac image sequences.

INA: Duration 36 months. Budget 15 kEuros. This contract was associated to theCIFRE grant of E. Veneau's Ph-D thesis which was concerned with video macro-segmentation and video content indexing.

Thales: We have been involved in a long-term collaboration with Thales Airborne System-s. This collaboration is mainly devoted to academic problems, related to surveillancesystems, and has been active in the following domains: target motion analysis, asso-ciation problems (plot-to-plot, plot-to-track and track-to-track), threat evaluation.This collaboration is emphasized by the supervision of two Ph.D theses, I. Leibowicz(2002) and F. Bavenco� (July 2005) as well as several articles (journal and confer-ences). Let us also mention the two CIFRE contracts corresponding to these twotheses (12 kEuros, for F. Bavenco�, 2002-2005).

Dupont De Nemours has provided (2005) an excellency unrestructed grant to E. M�eminto support his activities in the �eld of \Developing computational and visualizationcapabilities to extract object motion �elds and uid ow �elds from high-speedimaging". Budget: 6 kEuros.

2.14 Other funding, public, European, regional, ...:

2.14.1 Regional funding

� Video content understanding: Duration 36 months. Budget 91 Keuros. This contractsupplied the �nancial support of G. Piriou's Ph-D thesis.

� Motion detection: Duration 36 months. Budget 43 Keuros. This contract suppliedhalf of the �nancial support of T. Veit's Ph-D thesis.

� Robot system for 3D ultrasound image capture: Budget 68 Keuros. This contractsupplied part of the funding for the acquisition of this experimental set-up which isnow managed and exploited by the Lagadic and Visages teams.

2.14.2 National funding

� PRIAMM Mediaworks project: Duration 36 months. Budget 65 Keuros. The part-ners were LIMSI, TF1, Aegis company and Inria (Imedia, Texmex and Vista teams).The project was concerned with the development of an indexing and retrieval systemto assist documentalists. It mainly focused on the cooperation between media (imageand text) and on a semantic search engine. We have delivered several modules re-lated to video segmentation and to dynamic-content indexing. They were validatedon news programs.

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� RNTL Domus Videum: Duration 30 months. Budget 115 Keuros. The partners wereThomson-Multimedia, Irccyn, Irin, Inria (Metiss, Temics and Vista teams), INAand SFRS. This project was concerned with video archiving and browsing dedicatedto digital personal video recorders (PVR). We developed two methods for videosummarization of sports TV programs (including the game itself, advertisements,studio shots,. . . ) exploiting both visual and audio features. The �rst one involveda supervised learning stage and was applied to tennis video (selection of the mainwinning serves and most outstanding rallies). The second one is unsupervised andwas applied to team sports TV programs (football and rugby, selection of goals ortries and of the main actions). Very good results were obtained on a large set ofvideos.

� RIAM Feria: Duration 24 months. Budget 70 Keuros. The partners were INA, C&S,NDS and Vecsys companie, IRIT, Inria (Texmex and Vista teams), Arte. The projectbuilt a general and open framework allowing the easy development of applications forediting interactive video documents. We have developed several video segmentationand representation tools including a novel technique for selecting an appropriate setof key-frames to represent the visualized scene, a face tracking algorithm, and wecontributed to cross-modal analysis tools (video images, soundtrack, speech-to-texttranslations, texts). Extensive validation on opera video and TV shows programswere carried out with satisfactory results.

� Assimage project: ACI Ministry collaborative program on\Masse de donn�ees". Du-ration: 36 months (started in Sept. 2003). Budget: 30 kEuros. This project involvesthree Inria teams (Clime in Rocquencourt, Idopt in Grenoble, and Vista), threeCemagref groups (located in Rennes, Montpellier and Grenoble), the LEGI andLGGE laboratories both located in Grenoble. The aim is to develop methods forthe assimilation of images in mathematical models governed by partial di�erentialequations. The targeted applications concern predictions of geophysical ows. Ourcontribution is dealing with the tracking of vortex structures in collaboration withCemagref Rennes.

� Behaviour project: ACI Ministry collaborative program on Security and ComputerScience. Duration: 36 months, started Oct. 2004. Funding: 30 kEuros. Partners:Compi�egne University of Technology, Heudiasyc lab (prime), PSA-Peugeot-Citro�en(Innovation and Quality group). Goal: visual monitoring of car drivers, based onvideos shot inside the car, such that hypo-vigilant behaviors (mainly drowsiness anddistraction) can be detected

� ACI IMPBio: Modyncell5d project started in October 2004- duration of 36 months- budget allocated to Vista team is 28 Keuros - Partners are MIA Unit (AppliedMathematics and Informatics) from INRA (Jouy-en-Josas), Curie Institute, BiologyDpt of Univ. Rennes 1. The challenge is to track GFP tags with high precisionin movies to generate information about dynamics. Methods are developed for twoproteins: CLIP 170 involved in the kinetochores anchorage; Rab6a' involved in theregulation of transport from the Golgi apparatus to the endoplasmic reticulum.

� ACI DRAB: project related to the development of new uorescent probes started inoctober 2004 - duration of 36 months - budget allocated to Vista Team is 24 Keuros- Partners are Biology Dpt and Chemistry Dpt of Univ. Rennes 1. The project aimsat characterizing the +TIPs (plus-en tracking proteins) at the extremities \+" ofmicrotubules and their dynamics using new uorescent probes (Quantum Dots).

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� Within the 2001-2003 period, we were also involved in an ARC project in brain imageanalysis, the ACI Neurobase project, both conducted by C. Barillot (now with Vis-ages team), several Robea projects on robot vision and visual servoing conducted byF. Chaumette (now with Lagadic team), and the RIAM Sora project (with the SMETotal Immersion) concerned with augmented reality and conducted by E. Marchand(now with Lagadic team).

2.14.3 DGA (Defense Agency) funding

� DGA-Ens Cachan. Detection of trajectories of subpixellic objects in image se-quences. Budget: 23 kEuros over 11 months (April 2002-February 2003). Thepurpose was to study the feasability of a system detecting small fast moving targets.Our theoretical and practical results outperformed the one obtained by an industrialpartner of DGA.

� LRBA Posit contract. Duration: April 2001-July 2002. Budget: 85 kEuros. The aimof this project was to demonstrate the feasibility of terrain-aided navigation methodsbased on particle �ltering, coupled with inertial sensor. This project was managedby LRBA (Lab. Recherche Balistiques et A�erospatiales), Vernon, DGA and alsoinvolved ONERA and Cril company. More speci�cally, the Vista team was in chargeof the performance optimization. Results have been considered as satisfactory andsu�ciently promising to plan a new contract which would start in 2006.

� CEA/Cesta. Duration: March 2004-Oct. 2004. Budget: 29 kEuros. This projectwas centered around target acquisition. First, it was dealing with target tracking fora mobile during its reentry phase. Again, particle �lering and performance analysiswere instrumental, but the trajectory model is here highly complex, while the mainproblem was to estimate the time-varying ballistic coe�cient. In a second part, oure�orts focused on optimization of the measurement scheduling for target acquisitionduring the reeentry phase.

2.14.4 European funding

� Eumetsat contract on alternative tracking methods for derivation of atmosphericmotion vectors from Meteosat-8. Duration: 6 months (2004). Budget: 40 kEuros.The main objective of this project was to compare results of operational atmosphericmotion vectors currently used at Eutmesat with di�erent dense motion estimators.

� FP6-IST Fluid project: We are leader of this FET-IST project. Duration: 36 months(started in Dec. 2004). Budget: 328 kEuros. The FLUID project aims at studyingand developing new methods for the estimation, the analysis and the description ofcomplex uid ows from image sequences. It gathers �ve academic partners (Inria,Cemagref, University of Mannheim, University of Las Palmas de Gran Canaria andthe Laboratoire of M�et�eorologie Dynamique) and one industrial partner (La Vision)specialized in PIV visualization system.

� FP5-IST Carsense project: Duration 20 months. Budget 53 Keuros. The partnerswere Autocruise Ltd, BMW, CRF Societa Consortile per Azioni, Thomson-CSF De-texis, Jena-Optronik, Renault Recherche Innovation, IBEO Lasertechnik Hipp KG,TRW Automotive, l'ENSMP, le Livic-LCPC, l'Inrets-Leost et l'Inria (Imara, Sharpand Vistateams). This project was concerned with vehicle navigation assistancewith di�erent sensors. We developed an obstacle detection module based on imagemotion analysis.

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� FP5-IST Lava project: Duration 36 months (May 2002-April 2005). Budget 175 keu-ros. The partners are groups from computer vision, machine learning and cognitivesciences: XRCE (prime), IDIAP, Inria (Lear and Vista teams) RHUL, University ofGraz, University of Lund. The Lava (\Learning for Adaptable Visual Assistants")project has focused on two key problems : categorizing objects in static images andinterpreting events in video. We designed an original event-class learning approachbased on a robust partitional clustering algorithm. We have proposed several lo-calized motion descriptors, and de�ned a novel method to compare video segmentsbased on rank tests.

� European Network of Excellence Muscle. Vista is participating in the Network MUS-CLE (Multimedia Understanding through Semantics, Computation and Learning).The network started in March 2004 and will last 4 years. The allocated budget is160 kEuros(so far 42 kEuros received). Vista has particularly contributed (WP re-ports, talks in the periodic Muscle meetings, e-teams) to single modality extractionfor video analysis, cross-modal integration, and application of machine learning forclassi�cations in videos. P. Bouthemy organized a MUSCLE session for CBMI'2005.

� Visiontrain TN: Duration 4 years. Budget 24 kEuros. Visiontrain is a Marie CurieResearch Training Network which has just started in May 2005. Visiontrain ad-dresses the problem of understanding vision from both computational and cognitivepoints of view. It involves 11 partners and is headed by Movi team.

2.15 Teaching

Patrick Bouthemy: Master STI, Univ. Rennes 1, 10h per year (2001-2005) (image se-quence analysis); Master of Computer Science, Univ. Rennes 1, 2h per year (2003-2005) (video indexing); Master TIS (Signal and Image Processing) ENSEA-Univ.Cergy, 6h per year (2002-2004) (video analysis and indexing); Master PIC ENSPS-Univ. Strasbourg, 12h per year 2002-2005 (motion analysis).

Fr�ed�eric Cao: Master MVA (Mathematics, Vision, Learning) ENS Cachan, 20h per year(2002, 2003, 2004); Master STI, Univ. Rennes 1, 5h in 2004.

Charles Kervrann: ENSAI school, Rennes, 25 hours per year (2003, 2004, 2005) (statis-tical models and image analysis).

Jean-Pierre Le Cadre: Master STI, Univ. Rennes 1, 18 h. per year (2001-2005) (dis-tributed tracking, data association, MCMC methods).

Etienne M�emin: Master of Computer science, Univ. Rennes 1, 30 hours per year; MasterSTI, Univ. Rennes 1, 15h per year; DIIC Engineering degree, IFSIC, Rennes (150hper year).

We are tightly involved in two Masters belonging to the Doctoral school MATISSE ofUniversity of Rennes 1: STI (Signal, Telecommunications and Images) master andComputer Science master, as for organization and teaching tasks, Master students'trainees, a�liation of our Ph-D thesis students, Ph-D thesis grants (from the researchMinistery).

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2.16 Visibility

Patrick Bouthemy: Associate Editor of IEEE Trans. on Image Processing (1999-2003).Co-General Chair of ICCV'07. General Chair of CBMI'03 organized in Rennes. Co-TPC Chair of RFIA'06. TPC member of ICCV'03-05, CVPR'01-03-04, ECCV'06,ICPR'02-04, ICIP'03, ICME'04-05, CIVR'04-05, ACCV'04, CAIP'05, IBPRIA'05,ICARP'05, OTCBVS'05, SSIAI'02, RFIA'02-04. Member of the organizing commit-tee of SMVP'02. Head of the Scienti�c Committee of Irisa (\Comit�e des projets")from 1999 to 2002. Member of the \Commission d'Evaluation" of Inria from 1992to 2002 (and member of its steering committee 96-2002). Member of the BoardCommittee of the Technovision programme supported by the Ministry of Researchand by DGA and aiming at developing evaluation projects of image processing andcomputer vision techniques. Inria \main contact" for the French-German Quaeroprogram on multimedia indexing and retrieval. Deputy member of \Commission desp�ecialistes" in Signal and Image Processing at the University of Rennes 1. Invitedtalks at IWCVIA'03, Las Palmas, SCVMA'04, Prague. International Ph-D review-ing: N. Rea (Trinity College, Dublin), M. Barnard (IDIAP-EPFL), C. Dorea (UPC,Barcelona). Co-guest editor of a special issue of Multimedia Tools and Applicationson multimedia indexing (to appear).

Fr�ed�eric Cao: Member of the TPC of Scale-Space'2001, Scale-Space'2003, Gretsi'03,Gretsi'05, area chair in ISSP' 03, SSIAI'04, and organizer of the symposium Math-ematics, Image and Gestalt Theory in 2004. Invited talk at CVR'2005, Toronto.

Charles Kervrann: TPC member of ECCV'06, RFIA'06, ICCV'05, CAIP'05, ICME'05,CIVR'04, MICCAI'04, ICPR'04, CVPR'04. Member of the scienti�c council of the\Applied Mathematics and Informatics" Inra department.

Jean-Pierre Le Cadre: Area editor for the Journal of Advances in Information Fusion,member of the TPC of Fusion'01-05, TPC of COGIS'06, TPC of the NATO TargetTracking symposium'03. Deputy member of \Commission de sp�ecialistes" in Signaland Image Processing at the University of Rennes 1.

Etienne M�emin: TPC member of CVPR'04-05, ECCV'04-06, ICIP'03, ICCV'01, RFI-A'06. Deputy member of \Commission de sp�ecialistes" in Computer Science at theUniversity of Rennes 1. Co-organizer of a Dagstuhl seminar on experimental uidmechanics, computer vision and pattern recognition: a vision of the future (sched-uled in Spring 2007). Coordinator of the European FLUID project.

Patrick P�erez: Consultancy for Microsoft Research, Cambridge, UK. Associate Editorof IEEE Trans. on Image Processing (2000-2002). TPC member of Siggraph'04,ICPR'04, NIPS'04, CVPR'05, Siggraph'05, Eurographics'05, ICCV'05, CVPR'05,Coresa'05. Member of the scienti�c and technological orientation council (COST,workgroup on large scale inititiaves) of Inria. Head of the personnel committee(\Commission Personnel"), which oversees scienti�c non-permanent sta� hiring atIrisa/Inria-Rennes and in charge of the post-doc campaign for Inria-Rennes, 2005.Member of the direction team of Irisa/Inria-Rennes (\�Equipe de direction"). Demon-strations: Past work on interactive image editing, Microsoft Digital House, Paris,France, Oct. 2004; Tracking platform developed as part of the FTRD-CRE, FTRDInnovation Days held in Paris, June 2005. International Ph-D reviewing: F. Pitie(Trinity College).

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Jian-Feng Yao: Member of the executive comittee of MAS, a section of the French Societyof Applied and Industrial Mathematics, of the Commission de Sp�ecialistes (Hiringcommittee) for applied mathematics in several universities including University ofRennes and University of South Brittany.

Christian Barillot (now with Visages team) was General Chair of MICCAI'04.

2.17 Misc.:

Prizes and awards- F. Chaumette (now with Lagadic team) received with E. Malis the 2002 King SunFu Memorial Best IEEE Trans. on Robotics and Automation Paper Award.- R. Fablet and P. Bouthemy received the best paper award in pattern recognitionat RFIA'02.- J.-P. Le Cadre was awarded as Automatica outstanding reviewer.

3 Main evolution of the objectives during the evaluationperiod

3.1 Planned objectives

Due to the creation of the Lagadic and Visages teams, we do not indicate the objectivesformulated in 2001 which are now carried out by these two teams. The objectives givenin 2001 were thus the following ones.

� New approaches to dynamical pattern recognition and sequential state estimation

{ motion recognition at large (including deformations or activities)

{ statistical modeling of local spatio-temporal measurements

{ application challenges: automatic summarization of videos, direct interpreta-tion of dynamics scenes within speci�c contexts

{ pursuit of on-going research on multiple object tracking

� Physical measurements based on imaging dynamical phenomena

{ interest: qualitative and structural, spatial and temporal analysis (e.g., of tur-bulent ows)

{ incorporation of both physical models and physical measures (in collaboration)

{ extraction, characterization and tracking of interesting structures (e.g., vor-tices)

{ application challenges: powerful alternative to standard PIV techniques in ex-perimental uid mechanics (extension to 3D ows); new techniques for esti-mating wind �elds from meteorological satellite images; new domain: NDC ofstructures under strain.

� Evolution of project-team

{ proposition in 2001 of a new project, headed by P. Gros, on indexing an man-aging large digital multi-modal data-bases

{ proposition of setting in the mid-future a project on coupled perception-action,with F. Chaumette and E. Marchand

{ re-focusing of project-team on the analysis of dynamical phenomena (at large).

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3.2 Main evolution

The main evolution of the planned objectives is formed by additional objectives due to thearrival of F. Cao (Sept. 2001), C. Kervrann (July 2003) and P. P�erez (Feb. 2004). Theonly (minor) objective which was not ful�lled is the NDC of structures under strain forlack of availibility and of partnership. The additional objectives involve two new researchtopics (a contrario methods for image sequence analysis and biological video-microscopy)and the extension of the tracking topic, as described below.

� A contrario decision framework

According to the Bayesian Theory, if we su�ciently know what we look for, we willbe able to handle many situations. Moreover, high level knowledge may be integrat-ed. Some of the previous achievements in the Vista group followed this general lineand on-going parts of our research work still does. However, it is not that clear thatanything is learnable, or that prior knowledge is always necessary, if we admit thatlow-level vision makes sense. For instance, do we need a universal model of movingobject before detecting motion, or do we need a precise model of shapes to detectthat two images have shapes in common? In several applications, we have tried toprove that it is not the case. On the contrary, it is possible to calculate thresholdsthat make the detection very reliable, and decision very robust. The originality ofthe approach is that decision is established by rejecting a model representing the ab-sence of coherent information. This model can be de�ned analytically, unlike priorrealistic (but often simplistic) models that have to be learned. We have investigatedthis a contrario decision framework and extended it to spatio-temporal image anal-ysis problems, more speci�cally for motion detection, motion clustering and imagecomparison.

� Video-microscopy

Our objective is to develop methods for video-microscopy and molecular dynamicsanalysis which both exploit our expertise in image sequence processing and con-tribute to our research activities on motion detection, tracking, motion learning andinterpretation, while o�ering another type of image sequences, new challenges andapplications. Spatio-temporal denoising methods have been developed to removeadditive Gaussian noise or Poisson noise while preserving discontinuities. In ourapplications, we have mainly focused on the analysis of vesicles that deliver cellularcomponents to appropriate places within cells. Applications of the proposed imageprocessing methods to biological issues should provide a new and quantitative wayfor interpreting the movement of uorescently labeled membrane transport vesicles.

� Probabilistic visual tracking and SMC techniquesThe �rst methodological studies on SMC started in the group at the end of 1999,with the Ph.D work of Carine Hue which was dedicated to multi-target tracking withassociation and whose main experimental validations where in the context of bearingsonly tracking. In the last four yours, this trend has gained an increasing importancewith the Ph.D. of Elise Arnaud (2001-2004), Thomas Br�ehard (2002-2005), AnneCuzol (2003-) and the arrival of Patrick P�erez in Feb. 2004 after four years spentworking on these topics with Andrew Blake at Microsoft Research, Cambridge. Therange of studied methodological tools has thus expanded (sequential PCRB, Gibbssampling, Rao-Blackwellisation, hybrid �ltering, conditional �ltering, etc.) as well asthe range of applicative scenarios. In particular a variety of visual tracking problemsare now studied, including multi-cue adaptive tracking with no prior, luminancepattern tracking, physics-driven tracking in uid images.

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4 Objectives for the next four years

Our objectives for the next four years are mainly in the continuation of the research topicsas de�ned in 2004 following the creation of Lagadic and Visages teams. Signi�cativeextensions and some new subjects (due to the arrival of Ivan Laptev in 2005) are alsoenvisaged. They are organized in three main chapters.

4.1 Video analysis and understanding

One of the main goals of Vista is to develop methods for e�cient exploitation of videos,motivated by the fast growing amount of video data. Examples of our target applicationsinclude content-based video search, event detection and video summarization. In termsof video material, our speci�c interest is devoted to TV broadcasts such as TV shows,spectacles and particularly sport broadcasts. The reason for putting the latter forwardis (1) to exploit the expertise Vista has already acquired on this type of footage in thepast, through collaborative applied projects and (2) to instantiate the analysis work incontexts where real applications already exist and restricted context allows us to bene�tfrom domain-speci�c prior when generic prior-free approaches reach their limits. However,although applications might be less clear and less accessible for the time being, we wantto be open to other types of video material such as feature movies or home videos, forlonger term perspectives.

There exist numerous challenges and open research problems associated with the scopeof tasks mentioned above. Video data has multiple modes of variation originating fromthe changes in the view point, camera motion, illumination, background and, importantly,within-class variability of objects and events. This variability e�ects both the spatial andthe temporal structure of the video and has to be either suppressed or made explicit ifrelevant for the interpretation. Another problem is associated with the fragmentation ofvideo data both in time and space due to occlusions and the presence of multiple objectsand motions in the scene. Hence, traditional methods for temporal data processing suchas HMM cannot be applied directly unless strongly restricting the domain of application.Large quantities of video data, high (and possibly variable) dimensionality of features anda few available annotations create another problem associated with all existing learningtechniques, for example descriptive methods (HMM, PCA, KPCA, etc.) and discrimi-native methods (SVM, boosting, LDA, KDA, etc.). To address these and other relatedproblems we see the need of pushing forward research along the following directions.

a) Early analysis

In many applications of video analysis and processing, it is well known that a good ini-tialization is a keypoint, since the amount of data is too huge to be exhaustively explored.Hence, e�orts have to be spent on an early analysis of the scene, that is able to point outthe areas of the images where computationally speaking more e�cient but sometimes lessrobust algorithms could be applied. Elements from the perception of visual motion mayinspire new methods and algorithms for these preliminary detection steps. In particular,the detection of moving areas (independently of any semantics), the early detection ofocclusion and transparency seems to be a still unsolved while necessary step before ap-plying higher level algorithms as tracking, restoration, video-inpainting. It could also beused for measuring motion, since the most adequate method of estimation could be chosenaccordingly to the low-level geometrical con�guration.

b) Feature extraction and selection

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Video data contains rich and redundant information that depends on external conditionssuch as position and motion of the camera. An e�cient step to reduce redundancy andto suppress the irrelevant variation is to extract a set of features. We have previouslydeveloped di�erent types of global and local features and aim to extend this research inthe future (e.g., using more elaborated space-time primitives such as motion trajectories).A principle question, however, concerns the selection of features useful for one recognitiontask or the other. In this context we plan to investigate and adapt recently developedboosting methods for the task of feature selection in the context of recognizing events andactivities in video data.

c) Kernel-based methods for video analysis

Kernels-based methods (e.g. SVM) has proven as e�cient tools for static object recogni-tion. Direct application of these methods to video data is possible but may su�er fromseveral problems. When recognizing activities, for example, the result will depend on theselected start and end times as well as on the rate of execution that might not have alinear relation to the training data. To cope with these problems, we aim to investigateextensions of the current kernel-based methods such that to take a temporal structure ofthe video data into account.

d) Statistical models for recognition and segmentation

When using statistical image-based models for motion recognition, the data source in thetraining and in the test should correspond and should be separated from outliers. Forthis purpose a segmentation step is required. Since both segmentation and recognitionare interdependent tasks, methods for joint motion segmentation and recognition shouldbe developed. In this respect, merging statistical methods with the recently developede�cient methods for segmentation based on graph-cuts seems to be an interesting direc-tion for future research. The direct output of this research could be used for examplefor semi-automatic annotation of video data as well as for video editing with the task ofautomatic removal or substitution of objects with particular motion.

e) Video alignment

The aim of video alignment is to establish and verify space-time correspondence betweenvideos with identical or similar motion content while compensating for possible variationsin 3D view and independent camera motion. While 3D motion reconstruction is not in thefocus of this research (although it could be a subsequent step), 3D multi-view constraintsprovide e�cient means for matching motion under view variations. Due to the possiblyindependent camera motion, these constraints will be dynamic. Estimation of dynamicmulti-view constraints such as the time varying fundamental matrices from video data is acurrently open research topic. To address this problem we will explore the correspondencein the video data in terms of local motion and shape using space-time points features andtrajectories.

f) Semi-supervised learning

In a typical task of learning for video interpretation, the amount of necessary trainingdata is likely to be very large, while it is very di�cult to get clean labeled training data.Regarding the latter, even with, e.g., tracking tools, it seems more realistic to considersemi-supervised scenarios where training data is only partially, noisily and/or incompletelylabeled. Using tracking methods we aim to develop methods for semi-automatic annota-tion of video data and to substantially enlarge the amount of annotated video data. Wewill also investigate semi-supervised learning methods that take advantage of both labeled

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and unlabeled training video data.

g) Combined motion and object recognition

Video content often involves di�erent and interdependent modalities in terms of objects,motion, sound and speech. Dependence between them provides the source of additionalconstraints that can be used to improve the reliability of video interpretation. The depen-dence between static objects and motion is especially interesting since (a) the same type ofactivities often involve similar objects and (b) the recognition of objects can be improvedby recognizing the actions performed with them (e.g. visual similarity between a glassand a vase can be resolved by the drinking action performed with the glass). We aim toexplore this little developed �eld and to develop methods for joint action recognition andobject recognition by combining recent advances in both of these �elds.

h) Dynamic texture analysis

We want to further investigate the analysis of dynamic textures in videos of natural sceneswhile exploiting the mixed-stete (discrete and continuous) auto-models framework we haverecently de�ned. Envisaged extensions deal with larger neighborhoods, multi-scale mod-els, other continuous distributions (non zero-mean Gaussian distribution, beta distribu-tion,. . . ), in order to model long-distance spatial correlations and correlations betweencontinuous values. The addition of the time dimension in the auto-models is also an in-teresting extension. Beyond these modeling issues, the two major goals are to exploitthese mixed-state auto-models for the segmentation and the recognition/classi�cation ofdynamic textures in image sequences: separating dynamic texture from (possibly moving)background, distinguishing di�erent dynamic textures,. . .

The Vista team has no more permanent engineer since the departure of Fabien Spindlerto Lagadic team. This is a real issue for our developments in video applications (videoanalysis tools, video summarization, video indexing, video editing). The optimization,evaluation, management, exploitation and transfer of our video software modules cannotbe correctly ensured without the recruitment of a new permanent engineer.

4.2 Tracking

a) Visual tracking with no prior, that is, visual tracking of an arbitrary object within an

arbitrary video sequence.Robust generic tracking tools are of major interest for a wide range of applications dealingwith editing, analyzing, annotating, browsing and authoring video contents (see paragraph2.5.1). Even in applications where a strong prior is "available" (e.g., precise type ofvideos and/or type of objects of interest), such tools are crucial. On one hand, they areuseful complement to application speci�c detection/tracking methods that are often morecomplex (both in terms of preliminary o�-line learning and of on-line application) andsometimes less robust (e.g., false negatives of object-speci�c detectors). On the other hand,they can facilitate the semi-automatic extraction and labeling of data that application-speci�c learning modules will require. Despite the number of works, including by Vista,on this type of visual tracking problems, they remain largely unsolved. Such a trackingcannot rely, as classically done, on a priori information regarding both the appearance ofthe entities of interest (shape, texture, key views, etc.) and their visual motion (kinematicconstraints, expected dynamics relative to the camera, etc.).

The �rst crucial step is then the de�nition and the estimation of a reference appearancemodel on which the tracking, no matter its precise form, will rely on. This is very di�cult

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since one wants to �nd a representation that is discriminant enough to limit the amountof surrounding clutter, that is adapted on-line su�ciently fast such that rapid and drasticvisual appearance changes are captured while variations caused by occlusions are identi�edto avoid drift or loss. We believe that a good way to attack the problem is to combine, in away that might uctuate through time, complementary representations of the appearance(e.g., from detailed pixel-wise appearance model subject to rapid uctuations to roughcolor model very persistent over time) and making all of them evolve on-line. For thispurpose, it might be useful to mobilize feature selection and on-line learning techniques.Also, as already shown in E. Arnaud's thesis, the short-memory part of the appearancemodeling, which amounts to spatially localized instantaneous motion-estimation, can beincorporated in the evolution model.

However, the use of such sophisticated ingredients (incorporation of motion estimation,fusion of multiple cues, on-line learning and use of object detectors) does not �t nicely intothe standard state space, or HMM, approach (generative model composed of an observa-tion model and a data-free evolution model). As already suggested by E. Arnaud's work,more exible modeling paradigms, which still allow exact or approximate sequential stateestimation, should be sought. Couple or triple Markov chains proposed by Pieczynski andcolleagues could be an interesting alternative. More radical yet, turning to ConditionalMarkov models, known from the text analysis community and recently introduced in theimage community under the form of Conditional Markov Random Fields, could be veryfruitful.

b) High dimensionality in visual tracking applications

This is a complex problem for which an e�cient framework is lacking. We need to continueour recent research e�ort on that topic. For very large scale problems, some variationalassimilation techniques and several Bayesian smoothing approaches related to Kalman �l-tering have been proposed in meteorology or in oceanography. These techniques have notbeen really exploited in image processing and computer vision as the non linearity involvedin the measurement likelihood for visual tracking are usually multi-modal and highly nonlinear. Such di�culties require in practice to rely on particle �lter implementation ofnon-linear Bayesian �lter. Nevertheless, we believe there are possibilities to combine ad-vantageously these two kinds of �ltering for high dimensional state spaces associated toa precise physical dynamics. Such a cooperation could allow us to reduce the number ofparticles required in the non linear �lter implementation and in the same time to explorethe non linearity due to the unavoidable model imprecisions or simpli�cations. It shouldalso authorize a rougher de�nition of the initial condition associated to the assimilationtechnique.

c) Other items

First, it is worth stressing that initialization remains an important problem. This isespecially true for multitarget tracking in a dense environment, where association prob-lems are predominent. Performance analysis (essentially probability of correct association)remains an important area of future investigation. From an algorithmic viewpoint, combi-natorial optimization is now a "standard" tool. To a large extent, it is based on classical2D-assignment methods and Lagrangian relaxation. However, it is likely that simulation-based methods (e.g., Cross Entropy) would be a promising way as would be multicriteriaoptimization. More generally, a fundamental problem would be the evaluation of the com-binatorial complexity of elementary associations. To that aim, measures of combinatorialcomplexity should be developed, based on the evaluation of association performance (e.g.track purity, probability of correct association). We also believe that it is important to go

29

beyond tracking itself and to devote part of future e�orts on situation analysis for multiplemoving entities. This is especially true for camera network, where gathering elementaryinformations received at the camera level for obtaining a reliable picture of a dynamicsituation would be a considerable challenge.

4.3 Image sequence analysis for environmental, uid mechanics, andbiological studies

a) Fluid motion analysis

For uid motions analysis, we intend to pursue the research directions we have initiated thepast four years. The objective will be to further incorporate physical laws in the estimationtechniques. Nevertheless, as almost all of these laws live in a three-dimensional space wewill have to investigate 3D estimation from either 3D volumic data or from multiview 2Ddata. In addition, if we wish to address applications of greater complexity or to increaseour measurement accuracy we will have to explore more deeply the frontiers existingbetween image-based estimation methods and numerical modeling techniques solely basedon prede�ned initial condition data. Such a cooperation, which could be formalized withinthe data assimilation framework, should o�er us means to really couple models and featuresestimation from image data. For instance, one may envisage to provide 3D instantaneouswind �elds for several prede�ned low-cloud layers at a �ner scale than existing modelassimilation techniques do. We expect that image data and estimation methods wouldreplace the unknown boarder conditions and could deal with complex phenomena at �nerscales. Another important issue is to devise 3D image particle velocimetry techniquesfor uid mechanics experimental ows visualization. The dimensionality of the unknownvector �elds involved is of course a key point. We will have probably to further investigatelarge scale smoothing techniques such as the ensemble Kalman �lter, or the variationalassimilation techniques. In addition, we would like to extend the application domainswe are dealing with. We believe that our models for uid ows velocity estimation andanalysis could be applied to oceanography applications. A �rst promising contact hasbeen established with CLS in Toulouse.

Let us point out that the research work on uid motion analysis is mainly conductedby Etienne Memin who has important teaching duties as an assistant Professor at the uni-versity of Rennes. The recruitment of a junior research scientist is required to strengthenthis research area.

b) Video-microscopy

In video-microscopy, tracking methods that estimate trajectories of small objects (parti-cles) may encounter di�culties if the number of objects is large and the signal-to-noiseratio is low. Moreover, the tracked objects are not always visible in the sequence whentagging molecules separate suddenly from the target objects. Since data association isproblematic in that case, we plan to introduce speci�c mechanisms based on biologicalconcepts for tracking. Instead of tracking individual objects in the 3Dimage sequence,we propose to estimate the number of trips from one speci�c region labeled by biologiststo another one, to estimate dynamics. If the relationships between several regions aredescribed by a graph, we need to determine when an object leaves one region to reach an-other one. Furthermore, we can solve the detection problem to reliably count the numberof arrivals and departures from each region to another one over a period of time. Thisapproach seems more appropriate and relies on realistic concepts in intracellular dynam-ics as con�rmed by biologists. According to progress in video-microscopy, the dynamics

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of individual molecules described as a Wiener process (Brownian motion) with additiveGaussian noise and a compound Poisson process to model interactions with various typesof molecules, will be also considered for conventional individual spot traking. However,the estimation of the related motion parameters will be challenging when the number ofobjects is very large (high-dimensional problem).

The creation of a joint INRIA-INRA team in biological image processing could beenvisaged in a two or three-year term. It would require the recruitment a young researchscientist in order to form a team of a su�cient size.

Ph.D. Theses

[1] E. Arnaud. M�ethodes de �ltrage pour du suivi dans des s�equences d'images - Application ausuivi de points caract�eristiques. PhD thesis, Universit�e de Rennes 1, mention Traitement duSignal et T�el�ecommunications, November 2004.

[2] T. Br�ehard. Estimation s�equentielle et analyse de performances pour un probl�eme de �ltragenon lin�eaire partiellement observ�e. Application �a la trajectographie par mesure d'angles. PhDthesis, Universit�e de Rennes 1, Mention Traitement du Signal et des T�el�ecommunications,December 2005.

[3] I. Corouge. Mod�elisation statistique de formes en imagerie c�er�ebrale. PhD thesis, Universit�ede Rennes 1, mention Informatique, April 2003.

[4] T. Corpetti. Estimation et analyse de champs denses de vitesses d'�ecoulements uides. PhDthesis, Universit�e de Rennes 1, Mention Traitement du Signal, juillet 2002.

[5] F. Dambreville. Optimisation de la gestion des capteurs et des informations pour un sys-t�eme de d�etection. PhD thesis, Universit�e de Rennes 1, mention Traitement du signal ett�el�ecommunications, novembre 2001.

[6] F. Dekeyser. Restauration de s�equences d'images par des approches spatio-remporelles: �l-trage et super-r�esolution par le mouvement. PhD thesis, Universit�e de Rennes 1, mentionTraitement du signal et t�el�ecommunications, novembre 2001.

[7] R. Fablet. Mod�elisation statistique non param�etrique et reconnaissance du mouvement dansdes s�equences d'images ; application �a l'indexation vid�eo. PhD thesis, Universit�e de Rennes 1,mention Traitement du signal et t�el�ecommunications, juillet 2001.

[8] G. Flandin. Mod�elisation probabiliste et exploration visuelle autonome pour la reconstructionde sc�enes inconnues. PhD thesis, Universit�e de Rennes 1, mention Informatique, novembre2001.

[9] C. Hue. M�ethodes s�equentielles de Monte-Carlo pour le �ltrage non lin�eaire multi-objets dansun environnement bruit�e. Applications au pistage multi-cibles et �a la trajectographie d'entit�esdans des s�equences d'images 2D. PhD thesis, Universit�e de Rennes 1, mention Informatique,January 2003.

[10] I. Leibowicz. Traitements multicapteurs pour les syst�emes a�eroport�es de patrouille maritime.PhD thesis, Universit�e de Rennes 1, mention Traitement du signal et t�el�ecommunications,f�evrier 2001.

[11] Y. Mezouar. Plani�cation de trajectoires pour l'asservissement visuel. PhD thesis, Universit�ede Rennes 1, mention Informatique, novembre 2001.

[12] G. Piriou. Mod�elisation statistique du mouvement dans des s�equences d'images pour la recon-naissance de contenus dynamiques. PhD thesis, Universit�e de Rennes 1, Mention Traitementdu Signal et des T�el�ecommunications, December 2005.

[13] F. Rousseau. M�ethodes d'analyse d'images et de calibration pour l'�echographie 3D enmode main-libre. PhD thesis, Universit�e de Rennes 1, mention Traitement du Signal etT�el�ecommunications, December 2003.

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[14] T. Veit. D�etection et analyse du mouvement dans des s�equences d'images selon une approcheprobabiliste a contrario. PhD thesis, Universit�e de Rennes 1, Mention Traitement du Signalet des T�el�ecommunications, December 2005.

[15] E. Veneau. Macro-segmentation multi-crit�ere et classi�cation de s�equences par le contenudynamique pour l'indexation vid�eo. PhD thesis, Universit�e de Rennes 1, Mention Traitementdu Signal, f�evrier 2002.

H.d.R's

[1] F. Cao. Sur quelques probl�emes math�ematiques de l'analyse des formes. Habilitation �a dirigerdes recherches, Universit�e de Paris Dauphine, December 2004.

[2] E. M�emin. Estimation du ot optique : contributions et panorama de di��erentes approches.Habilitation �a diriger des recherches, Universit�e de Rennes 1, July 2003.

[3] P. P�erez. Mod�eles et algorithmes pour l'analyse probabiliste des images. Habilitation �a dirigerdes recherches, Universit�e de Rennes 1, December 2003.

Journals

[1] A. Almansa, F. Cao, Y. Gousseau, and B. Roug�e. Interpolation of digital elevation modelsusing AMLE and related methods. IEEE Transactions on Geoscience and Remote Sensing,40(2):314{325, February 2002.

[2] L. Amsaleg and P. Gros. Content-based retrieval using local descriptors: problems and issuesfrom a database perspective. Pattern Analysis and Applications, 4(2/3):108{124, 2001.

[3] E. Arnaud, E. M�emin, and B. Cernuschi-Frias. Conditional �lters for image sequence basedtracking - application to point tracking. IEEE Trans. on Image Processing, 14(1):63{79,2005.

[4] C. Baillard, P. Hellier, and C. Barillot. Segmentation of brain 3D MR images using levelsets and dense registration. Medical Image Analysis, 5(3):185{194, September 2001.

[5] F. Bavenco�, J.-M. Vanpeperstraete, and J.-P. Le Cadre. Constrained bearings-only targetmotion analysis via monte carlo markov chain methods. IEEE Trans. on Aerospace andElectronic Systems. to appear.

[6] Th. Br�ehard and J.-P. Le Cadre. Closed-form posterior Cram�er-Rao bound for bearings-onlytracking. IEEE Trans. on Aerospace and Electronic Systems. to appear.

[7] F. Cao. Application of the Gestalt principles to the detection of good continuations andcorners in image level lines. Computing and Visualisation in Science, 7:3{13, 2004.

[8] F. Cao and R. Fablet. Automatic morphological detection of otolith nucleus. Pattern Recog-nition Letters. to appear.

[9] F. Cao, P. Mus�e, and F. Sur. Extracting meaningful curves from images. Journal of Mathe-matical Imaging and Vision, 22(2-3):159{181, 2005.

[10] F. Chaumette. Image moments: a general and useful set of features for visual servoing. IEEETransactions on Robotics. accepted for publication, 2003.

[11] F. Chaumette and E. Marchand. A redundancy-based iterative approach for avoiding jointlimits: application to visual servoing. IEEE Trans. on Robotics and Automation, 17(5),October 2001.

[12] C. Collet, J.-N. Provost, P. Rostaing, P. P�erez, and P. Bouthemy. Segmentationbathym�etrique d'images multispectrales SPOT. Traitement du Signal, 18(1):1{16, January2001.

32

[13] C. Collewet and F. Chaumette. Positioning a camera with respect to planar objects ofunknown shape by coupling 2D visual servoing and 3D estimations. IEEE Trans. on Roboticsand Automation, 18(3):322{333, June 2002.

[14] I. Corouge, P. Hellier, B. Gibaud, and C. Barillot. Inter-individual functional mapping: anon linear local approach. Neuroimage, 19(4):1337{1348, August 2003.

[15] T. Corpetti, E. M�emin, and P. P�erez. Dense estimation of uid ows. IEEE Transactionson Pattern Analysis and Machine Intelligence, 24(3):365{380, March 2002.

[16] T. Corpetti, E. M�emin, and P. P�erez. Extraction of singular points from dense motion �elds :an analytic approach. Journal of Mathematical Imaging and Vision, 19(3):175{198, 2003.

[17] Th. Corpetti, D. Heitz, G. Arroyo, E. M�emin, and A. Santa-Cruz. Fluid experimental owestimation based on an optical- ow scheme. International Journal of Experiments in Fluid.to appear.

[18] N. Courty and E. Marchand. Navigation et controle d'une cam�era dans un environnementvirtuel: une approche r�ef�erenc�ee image. Technique et Science Informatiques, TSI, 20(6):779{803, June 2001.

[19] A. Criminisi, P. P�erez, and K. Toyama. Region �lling and object removal by exemplar-basedinpainting. IEEE Trans. on Image Processing, 13(9):1200{1212, 2004.

[20] A. Cr�etual and F. Chaumette. Application of motion-based visual servoing to target tracking.Int. Journal of Robotics Research, 20(11), November 2001.

[21] A. Cr�etual and F. Chaumette. Visual servoing based on image motion. Int. Journal ofRobotics Research, 20(11), November 2001.

[22] F. Dambreville and J.-P. Le Cadre. Detection of a markovian target with optimization of thesearch e�orts under generalized linear constraints. Naval Research Logistics, 49(2):117{142,February 2002.

[23] F. Dambreville and J.-P. Le Cadre. Constrained minimax optimization of continuous searche�orts for the detection of a stationary target. Naval Research Logistics. to appear.

[24] F. Dambreville and J.-P. Le Cadre. Spatio-temporal multi-mode management for movingtarget detection. Journal of Information Fusion, 5:169{178, 2004.

[25] R. Donati and J.-P. Le Cadre. Detection, target motion analysis and track association witha sensor network. Journal of Information Fusion. to appear.

[26] R. Donati and J.-P. Le Cadre. Detection of oceanic electric �elds based on the GLRT. IEEProc. Radar, Sonar and Navigation, 149(4):221{230, April 2002.

[27] R. Fablet and P. Bouthemy. Non-parametric scene activity analysis for statistical retrievalwith partial query. Journal of Mathematical Imaging and Vision, 14(3):257{270, May 2001.

[28] R. Fablet and P. Bouthemy. Motion recognition using non parametric image motion modelsestimated from temporal and multiscale cooccurrence statistics. IEEE Trans. on PatternAnalysis and Machine Intelligence, 25(12):1619{1624, December 2003.

[29] R. Fablet, P. Bouthemy, and P. P�erez. Non-parametric motion characterization using causalprobabilistic models for video indexing and retrieval. IEEE Trans. on Image Processing,11(4):393{407, April 2002.

[30] G. Flandin and F. Chaumette. Fusion d'informations visuelles pour la localisation d'objetscomplexes. Traitement du Signal, 19(1):49{57, June 2002.

[31] M. Gelgon, P. Bouthemy, and J.-P. Le Cadre. Recovery of the trajectories of multiple movingobjects in an image sequence with a PMHT approach. Image and Vision Computing Journal,23(1):19{31, 2005.

[32] P. Hellier and C. Barillot. A hierarchical parametric algorithm for deformable multimodal im-age registration. Computer Methods and Programs in Biomedicine. accepted for publication,2003.

33

[33] P. Hellier and C. Barillot. Coupling dense and landmark-based approaches for non rigidregistration. IEEE Transactions on Medical Imaging, 22(2):217{227, February 2003.

[34] P. Hellier, C. Barillot, I. Corouge, B. Gibaud, G. Le Goualher, D.L. Collins, A. Evans,G. Malandain, N. Ayache, G.E. Christensen, and H.J. Johnson. Retrospective evaluation ofinter-subject brain registration. IEEE Transactions on Medical Imaging, 22(9):1120{1130,2003.

[35] P. Hellier, C. Barillot, E. M�emin, and P. P�erez. Hierarchical estimation of a dense deformation�eld for 3D robust registration. IEEE Transactions on Medical Imaging, 20(5):388{402, 2001.

[36] L. Hubert-Moy, A. Cotonnec, L. Le Du, A. Chardin, and P. P�erez. A comparison of para-metric classi�cation procedures of remotly sensed data applied on di�erent landscape units.Remote Sensing of Environment, 75(2):174{187, February 2001.

[37] C. Hue, J.-P. Le Cadre, and P. P�erez. Posterior Cramer-Rao bounds for multi-target tracking.IEEE Trans. on Aerospace and Electronic Systems. to appear.

[38] C. Hue, J.-P. Le Cadre, and P. P�erez. Sequential Monte Carlo methods for multiple targettracking and data fusion. IEEE Trans. on Signal Processing, 50(2):309{325, February 2002.

[39] C. Hue, J.-P. Le Cadre, and P. P�erez. Tracking multiple objects with particle �ltering. IEEETrans. on Aerospace and Electronic Systems, 38(3):791{812, July 2002.

[40] C. Kervrann, D. Leland, and L. Pardini. Robust incremental compensation of the lightattenuation with depth in 3d uorescence microscopy. J. Microscopy, 214(3):297{314, 2004.

[41] A. Kokaram, N. Rea, R. Dahyot, M. Tekalp, P. Bouthemy, P. Gros, and I. Sezan. Browsingsports video - the challenge of choice. IEEE Signal Processing Magazine. to appear.

[42] B. Lamiroy, P. Gros, and S. Picard. Combining local recognition methods for better imagerecognition. Vision, 17(2):1{6, Second Quarter 2001.

[43] I. Laptev. On space-time interest points. International Journal of Computer Vision,64(2/3):107{123, 2005.

[44] J.-P. Le Cadre. Quelques aper�cus sur l'optimisation de la r�epartition des e�orts de recherched'une cible. Traitement du Signal, num�ero sp�ecial \Gestion intelligente des senseurs". toappear.

[45] E. Malis and F. Chaumette. Theoretical improvements in the stability analysis of a newclass of model-free visual servoing methods. IEEE Trans. on Robotics and Automation,18(2):176{186, April 2002.

[46] E. Malis, G. Morel, and F. Chaumette. Robot control from disparate multiple sensors. Int.Journal of Robotics Research, 20(5):364{378, May 2001.

[47] E. Marchand, P. Bouthemy, and F. Chaumette. A 2D-3D model-based approach to real-timevisual tracking. Image and Vision Computing, 19(13):941{955, November 2001.

[48] E. Marchand, F. Chaumette, F. Spindler, and M. Perrier. Controlling an uninstrumentedmanipulator by visual servoing. The International Journal of Robotics Research, IJRR,21(7):635{648, July 2002.

[49] E. Marchand and N. Courty. Controlling a camera in a virtual environment. The VisualComputer Journal, 18(1):1{19, February 2002.

[50] Y. Mezouar and F. Chaumette. Avoiding self-occlusions and preserving visibility by pathplanning in the image. Robotics and Autonomous Systems, 41(2-3):77{87, November 2002.

[51] Y. Mezouar and F. Chaumette. Path planning for robust image-based control. IEEE Trans.on Robotics and Automation, 18(4):534{549, August 2002.

[52] Y. Mezouar and F. Chaumette. Optimal camera trajectory with image-based control. Inter-national Journal of Robotics Research, 22(10/11):781{804, October 2003.

34

[53] E. M�emin and P. P�erez. Hierarchical estimation and segmentation of dense motion �elds.Int. Journal of Computer Vision, 46(2):129{155, February 2002.

[54] E. M�emin and T. Risset. Vlsi design methodoloy for edge-preserving image reconstruction.Real Time Imaging, 7(1):109{126, February 2001.

[55] L. Oisel, E. M�emin, L. Morin, and F. Galpin. 1D dense disparity estimation for 3D recon-struction. IEEE Trans. on Image Processing, 12(9):1107{1119, September 2003.

[56] C. Papin, P. Bouthemy, and G. Rochard. Unsupervised segmentation of low clouds frominfrared METEOSAT images based on a contextual spatio-temporal labeling approach. IEEETrans. on Geoscience and Remote Sensing, 40(1):104{114, January 2002.

[57] P. P�erez, J. Vermaak, and A. Blake. Data fusion for visual tracking with particles. Proceedingsof IEEE, 92(3):495{513, 2004.

[58] N. Peyrard and P. Bouthemy. Motion-based selection of relevant video segments for videosummarization. Multimedia Tools and Applications, 26(3):259{276, 2005.

[59] I. Pratikakis, C. Barillot, P. Hellier, and E. M�emin. Robust multiscale deformable registrationof 3d ultrasound images. International Journal of Image and Graphics, 3(4):547{565, October2003.

[60] J.-N. Provost, C. Collet, P. Rostaing, P. P�erez, and P. Bouthemy. Hierarchical Markoviansegmentation of multispectral images for the reconstruction of water depth maps. ComputerVision and Image Understanding, 93(2):155{174, 2004.

[61] T. Veit, F. Cao, and P. Bouthemy. An a contrario decision framework for region-basedmotion detection. International Journal of Computer Vision. to appear.

[62] J. Vermaak, S. Godsill, and P. P�erez. Monte Carlo �ltering for multi-target tracking anddata association. IEEE Trans. on Aerospace and Electronic Systems, 41(1):309{332, 2005.

Conference proceedings

[1] A. Alhaj, C. Collewet, and F. Chaumette. Couplage entre asservissement visuel et recon-struction 3d par vision dynamique. In 19e colloque GRETSI sur le traitement du signal etdes images, volume 1, pages 181{184, Paris, September 2003.

[2] A. Alhaj, C. Collewet, and F. Chaumette. Visual servoing based on dynamic vision. InIEEE Int. Conf. on Robotics and Automation, ICRA'03, volume 3, pages 3055{3060, Taipeh,Taiwan, May 2003.

[3] L. Amsaleg, P. Gros, and S.A. Berrani. A robust technique to recognize objects in images,and the DB problems it raises. In Proc. of the Workshop on Multimedia Information Systems,Capri, November 2001.

[4] E. Arnaud, B. Fauvet, E. M�emin, and P. Bouthemy. A robust and automatic face trackerdedicated to broadcast videos. In Proc. IEEE Int. Conf. on Image Processing (ICIP'05),Genova, Italy, September 2005.

[5] E. Arnaud and E. M�emin. Optimal importance sampling for tracking in image sequences:application to point tracking. In European Conference on Computer Vision, ECCV'04,Prague, Czech Republic, May 2004.

[6] E. Arnaud and E. M�emin. An e�cient rao-blackwellised particle �lter for object tracking.In Proc. IEEE Int. Conf. on Image Processing (ICIP'05), Genova, Italy, September 2005.

[7] E. Arnaud, E. M�emin, and B. Cernushi Frias. Filtrage conditionnel pour la trajectographiedans des s�equences d'images - application au suivi de points. In 14�eme Congr�es FrancophoneAFRIF-AFIA de Reconnaissance des Formes et Intelligence Arti�cielle, RFIA'04, Toulouse,France, January 2004.

35

[8] E. Arnaud, E. M�emin, and B. Cernushi Frias. A robust stochastic �lter for point tracking inimage sequences. In Asian Conference on Computer Vision, ACCV'04, Jeju Island, Korea,January 2004.

[9] V. Auvray, P. Bouthemy, and J. Lienard. Estimation param�etrique multir�esolution de mou-vements transparents. In GRETSI'2005, Louvain-la-Neuve, Belgium, September 2005.

[10] V. Auvray, P. Bouthemy, and J. Lienard. Multiresolution parametric estimation of trans-parent motions. In Proc. IEEE Int. Conf. on Image Processing (ICIP'05), Genova, Italy,September 2005.

[11] V. Auvray, J. Lienard, and P. Bouthemy. Multiresolution parametric estimation of transpar-ent motions and denoising of uoroscopic images. In Int. Conf. on Medical Image Computingand Computer Assisted Intervention (MICCAI'05), Palm Springs, USA, October 2005.

[12] C. Barillot, P. Hellier, and I. Corouge. Non rigid registration in neuroimaging: from aretrospective evaluation of global approaches to new advances in local and hybrid methods.In IEEE Int. Symp. on Biomedical Imaging, Washington, D.C., sept. 2002.

[13] F. Bavenco�, J.-M. Vanpeperstraete, and J.-P. Le Cadre. Performance analysis of optimaldata association within a linear regression framework. In 8th International Conference onInformation Fusion, Philadelphia, PA, USA, 2005.

[14] A. Benzougar, P. Bouthemy, and R. Fablet. Mrf-based moving object detection from mpegcoded video. In IEEE Int. Conf. on Image Processing, ICIP'2001, pages 402{405, Thessa-lonique, October 2001.

[15] J. Boulanger, Ch. Kervrann, and P. Bouthemy. Adaptive spatio-temporal restoration for 4d uoresence microscopic imaging. In Int. Conf. on Medical Image Computing and ComputerAssisted Intervention (MICCAI'05), Palm Springs, USA, October 2005.

[16] J. Boulanger, Ch. Kervrann, and P. Bouthemy. An adaptive statistical method for 4d- uorescence image sequence denoising with spatio-temporal discontinuities preserving. InProc. of Imaging for Medical and Life Sciences (IMVIE2), Illkirch, France, March 2005.

[17] J. Boulanger, Ch. Kervrann, and P. Bouthemy. An adaptive statistical method for denoising4d uorescence image sequences with preservation of spatio-temporal discontinuities. In Proc.IEEE Int. Conf. on Image Processing (ICIP'05), Genova, Italy, September 2005.

[18] P. Bouthemy. 2d motion description and contextual motion analysis: issues and new models.In Proc. Int. Workshop on Spatial Coherence for Visual Motion Analysis, SCVMA'2004,Prague, Czech Republic, May 2004.

[19] T. Br�ehard and J.-P. Le Cadre. A new approach for the bearings-only problem: estima-tion of the variance-to-range ratio. In 7th International Conference on Information Fusion,Stockholm, Sweden, 2004.

[20] T. Br�ehard and J.-P. Le Cadre. Un probl�eme de �ltrage non lin�eaire avec variance d'�etatinconnue: le pistage mono-cible par mesure d'angle seul. In XXXVI�emes Journ�ees de Statis-tique de la SFDS, Montpellier, France, 2004.

[21] T. Br�ehard and J.-P. Le Cadre. Distributed target tracking for nonlinear systems: appli-cation to bearings-only tracking. In Proc. Int. Conf. Information Fusion (FUSION'05),Philadelphia, USA, 2005.

[22] T. Br�ehard, J.-P. Le Cadre, M. Prenat, and Y. Cojan. Initialization of particle �lter andposterior cramer-rao bound for bearings-only tracking in modi�ed polar coordinate system. InSymposium on Target Tracking and Sensor Data Fusion for Military Observation, Budapest,Hungary, October 2003.

[23] Th. Br�ehard and J.-P. Le Cadre. Estimation distribu�ee pour un probl�eme de �ltrage nonlin�eaire. In XXXVII�emes Journ�ees de Statistique de la SFDS, Pau, France, 2005.

[24] F. Cao. Good continuations in digital image level lines. In IEEE Int. Conf. on ComputerVision, ICCV'03, pages 440{447, Nice, France, October 2003.

36

[25] F. Cao and R. Fablet. Automatic morphological detection of otolith nucleus. In Proc. Int.Conf. Pattern Recognition, ICPR'04, Cambridge, UK, August 2004.

[26] F. Chaumette. A �rst step toward visual servoing using image moments. In IEEE/RSJ IntConf on Intelligent Robots and Systems, IROS'2002, volume 1, pages 378{383, Lausanne,October 2002.

[27] F. Chaumette and A. Cr�etual. Dynamic stabilization of a pan and tilt camera for sub-marineimage visualization. In IARP Workshop on Underwater Robotics for Sea Exploitation andEnvironmental Monitoring, pages 146{157, Rio de Janeiro, October 2001.

[28] F. Coldefy and P. Bouthemy. Unsupervised soccer video abstraction based on pitch, dominantcolor and camera motion analysis. In Proc. ACM Multimedia 2004, New-York, October 2004.

[29] F. Coldefy, P. Bouthemy, M. Betser, and G. Gravier. Tennis video abstraction from audio andvisual cues. In Proc. IEEE Int. Workshop on Multimedia Signal Processing, MMSP'2004,Siena, Italy, Sept. 2004.

[30] C. Collewet, F. Chaumette, and P. Loisel. Image-based visual servoing on planar objects ofunknown shape. In IEEE Int. Conf. on Robotics and Automation, volume 1, pages 247{252,S�eoul, May 2001.

[31] A. Comport, E. Marchand, and F. Chaumette. A real-time tracker for markerless augmentedreality. In ACM/IEEE Int. Symp. on Mixed and Augmented Reality, ISMAR'03, pages 36{45,Tokyo, Japan, October 2003.

[32] A. Comport, M. Pressigout, E. Marchand, and F. Chaumette. A visual servoing controllaw that is robust to image outliers. In IEEE Int. Conf. on Intelligent Robots and Systems,IROS'03, Las Vegas, Nevada, October 2003.

[33] I. Corouge and C. Barillot. Use of a probabilistic shape model for non-linear registrationof 3d scattered data. In IEEE Int. Conf. on Image Processing, ICIP'2001, pages 149{152,Thessalonique, October 2001.

[34] I. Corouge, C. Barillot, P. Hellier, P. Toulouse, and B. Gibaud. Non-linear local registra-tion of functional data. In M.-A. Viergever, T. Dohi, and M. Vannier, editors, MedicalImage Computing and Computer Assisted Intervention 2001 (MICCAI'01), Lecture Notesin Computer Sciences 2208, pages 948{956, Utrecht, October 2001.

[35] I. Corouge and C. Barillot. Statistical modeling of pairs of sulci in the context of neuroimagingprobabilistic atlas. In Conf. Medical Image Computing and Computer Assisted Intervention,MICCAI'2002, volume Vol. LNCS 2489, pages 655{662, Tokyo, September 2002.

[36] I. Corouge, M. Dojat, and C. Barillot. Statistical shape modeling of unfolded retinotopicmaps for a visual areas probabilistic atlas. In Medical Image Computing and ComputerAssisted Intervention, MICCAI'03, Montreal, Canada, November 2003.

[37] I. Corouge, P. Hellier, P. Toulouse, B. Gibaud, and C. Barillot. A new inter-individualfunctional mapping scheme based on a local non linear registration method. In Proc. ofHuman Brain Mapping, HBM'2001, Neuroimage, volume 16, pages 100{100, Brighton, June2001.

[38] T. Corpetti, E. M�emin, A. Santa-Cruz, D. Heitz, and G. Arroyo. Optical ow estimation inexperimental uid mechanics. In Seventh Int. Symp. on Signal Processing and its Applica-tions, ISSPA'03, Paris, France, July 2003.

[39] T. Corpetti, E. M�emin, and P. P�erez. Estimation dense du mouvement en imagerie uide. In9�eme Colloque Francophone de Visualisation et de Traitement d'Images en M�ecanique desFluides, FLUVISU'2001, Rouen, June 2001.

[40] T. Corpetti, E. M�emin, and P. P�erez. Dense motion analysis in uid imagery. In EuropeanConf. on Computer Vision, ECCV'2002, volume LNCS 2350, pages 676{691, Copenhague,May 2002.

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[41] T. Corpetti, E. M�emin, and P. P�erez. R�egularisation div-curl et �equation de continuit�epour l'estimation du mouvement uide. In 13�eme Congr�es Francophone AFRIF-AFIA deReconnaissance des Formes et Intelligence Arti�cielle, RFIA'2002, volume 3, pages 887{898,Angers, January 2002.

[42] N. Courty, F. Lamarche, S. Donikian, and E. Marchand. A cinematography system forvirtual storytelling. In O. Balet, G. Subsol, and P. Torguet, editors, Int. Conf. on VirtualStorytelling, ICVS'03, Lecture Notes in Computer Science, Toulouse, France, November2003.

[43] N. Courty and E. Marchand. Computer animation: a new application for image-based visualservoing. In IEEE Int. Conf. on Robotics and Automation, volume 1, pages 223{228, S�eoul,May 2001.

[44] N. Courty and E. Marchand. Visual perception based on salient features. In IEEE Int. Conf.on Intelligent Robots and Systems, IROS'03, Las Vegas, Nevada, October 2003.

[45] N. Courty, E. Marchand, and B. Arnaldi. Through-the-eyes control of a virtual humano��d.In H.-S. Ko and N. Magnenat-Thalmann, editors, IEEE Int. Conf. on Computer Animation2001, S�eoul, November 2001.

[46] N. Courty, E. Marchand, and B. Arnaldi. A new application for salliency maps: Syntheticvision of autonomous actors. In IEEE Int. Conf. on Image Processing, ICIP'03, Barcelona,Spain, September 2003.

[47] A. Cuzol, P. Hellier, and E. M�emin. A novel parametric method for non-rigid image regis-tration. In G. Christensen and M. Sonka, editors, Proc. Information Processing in MedicalImaging (IPMI'05), number 3565 in LNCS, pages 456{467, Glenwood Springes, Colorado,USA, July 2005.

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[49] A. Cuzol and E. M�emin. Vortex and source particles for uid motion estimation. In 5th Int.Conf. on Scale-Space and PDE methods in Computer Vision (Scale-Space'05), Hofgeismar,Germany, 2005.

[50] F. Dambreville and J.-P. Le Cadre. Distribution of continuous search e�ort for the detectionof a target with optimal moving strategy. In SPIE Conf. on Data and Signal Processing ofSmall Targets, San Diego, July 2001.

[51] F. Dambreville and J.-P. Le Cadre. Optimal distribution of continuous search e�ort for detec-tion of a target in a min-max game context. In 4th International Conference on InformationFusion, FUSION'01, Montr�eal, August 2001.

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[55] Minvielle P. Dodin, P. and J.-P. Le Cadre. Re-entry vehicle tracking, observability andtheoretical bound. In 8th International Conference on Information Fusion, Philadelphia,PA, USA, July 2005.

[56] R. Fablet and P. Bouthemy. Motion recognition using spatio-temporal random walks insequence of 2D motion-related measurements. In IEEE Int. Conf. on Image Processing,ICIP'2001, pages 652{655, Thessalonique, October 2001.

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[57] R. Fablet and P. Bouthemy. Non parametric motion recognition using temporal multi-scale gibbs models. In IEEE Int. Conference on Computer Vision and Pattern Recognition,CVPR'01, Kauai Marriott, Hawaii, December 2001.

[58] R. Fablet and P. Bouthemy. Extraction of regions of interest based on motion activityfor video retrieval with partial queries. In 9th Int. Conf. on Information Processing andManagement of Uncertainty in Knowledge-Based Systems, IPMU'2002, Annecy, July 2002.

[59] R. Fablet and P. Bouthemy. Mod�elisation statistique non param�etrique et reconnaissance dumouvement. In 13�eme Congr�es Francophone AFRIF-AFIA de Reconnaissance des Formeset Intelligence Arti�cielle, RFIA'2002, volume 2, pages 549{558, Angers, January 2002.

[60] B. Fauvet, P. Bouthemy, P. Gros, and F. Spindler. A geometrical key-frame selection methodexploiting dominant motion estimation in video. In International Conference on Image andVideo Retrieval, CIVR'04, Dublin, Ireland, July 2004.

[61] G. Flandin and F. Chaumette. Autonomous visual exploration of complex objects. InIEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'01, Maui, Hawai, October2001.

[62] G. Flandin and F. Chaumette. Vision-based control using probabilistic geometry for objectsrepresentation. In IEEE Int. conf. on Decision and Control, CDC'01, Orlando, December2001.

[63] G. Flandin and F. Chaumette. Visual data fusion for objects localization by active vision.In European Conf. on Computer Vision, ECCV'2002, volume LNCS 2353, pages 312{326,Copenhague, May 2002.

[64] D. Heitz, J. Carlier, and E. M�emin. Fluid dedicated optical- ow scheme. In Proc. Int. Symp.on Particle Image Velocimetry (PIV'05), Pasadena, CA, September 2005.

[65] P. Hellier. Consistent intensity correction of MR images. In IEEE conf. on Image Processing,ICIP'03, Barcelona, Spain, September 2003.

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[73] C. Kervrann. An adaptive window approach for image smoothing and structures preserving.In European Conference on Computer Vision, ECCV'04, Prague, Czech Republic, May 2004.

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[78] C. Lacoste, R. Fablet, P. Bouthemy, and Y.-F. Yao. Cr�eation de r�esum�es de vid�eos par uneapproche statistique. In 13�eme Congr�es Francophone AFRIF-AFIA de Reconnaissance desFormes et Intelligence Arti�cielle, RFIA'2002, volume 1, pages 153{162, Angers, January2002.

[79] I. Laptev, S. Belongie, P. P�erez, and J. Wills. Periodic motion detection and segmentationvia approximate sequence alignment. In Proc. Int. Conf. on Computer Vision (ICCV'05),Beijing, China, October 2005.

[80] F. Lauze, P. Kornprobst, and E. M�emin. A multiscale approach for linear least-square basedoptical ow estimation. In Proc. British Machine Vision Conference, London, September2004.

[81] J.-P. Le Cadre. Allocation of search e�ort to optimize information. In 4th InternationalConference on Information Fusion, FUSION'01, Montr�eal, August 2001.

[82] J.-P. Le Cadre. Approximations de la probabilit�e de d�etection d'une cible mobile. In ColloqueGRETSI sur le Traitement du Signal et des Images,, Toulouse, September 2001.

[83] J.-P. Le Cadre. Detection of a target moving in a network. In Fusion'2003, Cairns, Australia,July 2003.

[84] G. Lefaix, E. Marchand, and P. Bouthemy. Motion-based obstacle detection and tracking forcar driving assistance. In IAPR Int. Conf. on Pattern Recognition, ICPR'2002, volume 4,pages 74{77, Qu�ebec, August 2002.

[85] M. Letteboer, P. Willems, P. Hellier, and W. Niessen. Acquisition of 3d ultrasound imagesduring neuronavigation. In H.-U. Lemke and M.-W. Vannier, editors, Proc. of ComputerAssisted Radiology and Surgery, Paris, June 2002.

[86] J.-F. Lots, D. Lane, E. Trucco, and F. Chaumette. A 2-d visual servoing for underwatervehicle station keeping. In IEEE Int. Conf. on Robotics and Automation, volume 3, pages2767{2772, S�eoul, May 2001.

[87] R. Mahony, P. Corke, and F. Chaumette. Choice of image features for depth-axis control inimage based visual servo control. In IEEE/RSJ Int Conf on Intelligent Robots and Systems,IROS'2002, volume 1, pages 390{395, Lausanne, October 2002.

[88] R. Mahony, T. Hamel, and F. Chaumette. A decoupled image space approach to visualservo control of a robotic manipulator. In IEEE Int. Conf. on Robotics and Automation,ICRA'2002, volume 3, pages 3781{3786, Washington DC, May 2002.

[89] E. Marchand, F. Chaumette, F. Spindler, and M. Perrier. Controlling an uninstrumented rovmanipulator by visual servoing. InMTS/IEEE OCEANS 2001 Conference, pages 1047{1053,Honolulu, November 2001.

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[90] E. Marchand, F. Chaumette, F. Spindler, and M. Perrier. Controlling the manipulator of anunderwater ROV using a coarse calibrated pan tilt camera. In IEEE Int. Conf. on Roboticsand Automation, volume 3, pages 2773{2778, S�eoul, 2001.

[91] E. Marchand and F. Chaumette. Virtual visual servoing: a framework for real-time augment-ed reality. In G. Drettakis and H.-P. Seidel, editors, EUROGRAPHICS'2002 ConferenceProc., volume 21(3) of Computer Graphics Forum, pages 289{298, Sarrebruck, September2002.

[92] E. Marchand, J. Royan, and F. Chaumette. Calcul de pose et calibration par asservissementvisuel virtuel : application �a la r�ealit�e augment�ee. In 13�eme Congr�es Francophone AFRIF-AFIA de Reconnaissance des Formes et Intelligence Arti�cielle, RFIA'2002, volume 3, pages839{848, Angers, January 2002.

[93] Y. Mezouar and F. Chaumette. Avoiding self-occlusions and preserving visibility by pathplanning in the image. In 9th Int. Symp. on Intelligent Robotic systems, SIRS'2001, Toulouse,July 2001.

[94] Y. Mezouar and F. Chaumette. Design and tracking of desirable trajectories in the imagespace by integrating mechanical and visibility constraints. In IEEE Int. Conf. on Roboticsand Automation, volume 1, pages 731{736, S�eoul, May 2001.

[95] Y. Mezouar and F. Chaumette. Model-free optimal trajectories in the image space: appli-cation to robot vision control. In IEEE Int. Conference on Computer Vision and PatternRecognition, CVPR'01, Kauai Marriott, Hawai, December 2001.

[96] Y. Mezouar and F. Chaumette. Visual servoing by path planning. In European Conf. onControl, ECC'01, pages 2904{2909, Porto, September 2001.

[97] Y. Mezouar, A. Remazeilles, P. Gros, and F. Chaumette. Image interpolation for image-based control under large displacement. In IEEE Int. Conf. on Robotics and Automation,ICRA'2002, volume 3, pages 3787{3794, Washington DC, May 2002.

[98] P. Mus�e, F. Sur, F. Cao, and Y. Gousseau. Unsupervised thresholds for shape matching. InIEEE Int. Conf. on Image Processing, ICIP'2003, Barcelona, Spain, September 2003.

[99] N. Papadakis, E. M�emin, and F. Cao. A variational approach for object contour tracking.In Proc. ICCV'05 Workshop on Variational, Geometric and Level Set Methods in ComputerVision, Beijing, China, October 2005.

[100] S. Paris and J.-P. Le Cadre. Plani�cation for terrain-aided navigation. In Conf. Fusion'2002,pages 1007{1014, Annapolis, July 2002.

[101] N. Peyrard and P. Bouthemy. Content-based video segmentation using statistical motionmodels. In British Machine Vision Conference, BMVC'2002, volume 2, pages 527{536,Cardi�, September 2002.

[102] N. Peyrard and P. Bouthemy. Towards extraction of meaningful temporal video segments. In4th Int. Workshop on Multimedia Information Retrieval, MIR'2002, Juan-les-Pins, December2002.

[103] N. Peyrard and P. Bouthemy. Detection of meaningful events in videos based on a supervisedclassi�cation approach. In IEEE Int. Conf. on Image Processing, ICIP 2003, Barcelona,Spain, September 2003.

[104] N. Peyrard and P. Bouthemy. Motion-based selection of relevant video segments for videosummarisation. In IEEE Int. Conf. on Multimedia & Expo, ICME'2003, Baltimore, July2003.

[105] G. Piriou, P. Bouthemy, and J-F. Yao. Extraction of semantic dynamic content fromvideos with probabilistic motion models. In European Conf. on Computer Vision, ECCV'04,Prague, Czech Republic, May 2004.

[106] G. Piriou, P. Bouthemy, and J.-F. Yao. Learned probabilistic image motion models for eventdetection in videos. In Proc. Int. Conf. Pattern Recognition, ICPR'04, Cambridge, UK,August 2004.

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[107] G. Piriou, P. Bouthemy, and J-F. Yao. Motion content recognition in video database withmixed-state probabilistic causal models. In Int. Workshop on Content-Based MultimediaIndexing, CBMI'2005, Riga, June 2005.

[108] G. Piriou, F. Coldefy, P. Bouthemy, and J-F. Yao. D�etection supervis�ee d'�ev�enements �al'aide d'une mod�elisation probabiliste du mouvement per�cu. In 14�eme Congr�es Franco-phone AFRIF-AFIA de Reconnaissance des Formes et Intelligence Arti�cielle, RFIA 2004,Toulouse, France, January 2004.

[109] I. Pratikakis, C. Barillot, and P. Hellier. Robust multi-scale non-rigid registration of 3Dultrasound images. In M. Kerckhove, editor, 3rd Int. Conf. on Scale-Space and Morphologyin Computer Vision, Scale-Space'01, LNCS 2106, pages 389{397, Vancouver, July 2001.

[110] P. P�erez, C. Hue, J. Vermaak, and M. Gangnet. Color-based probabilistic tracking. In Eu-ropean Conf. on Computer Vision, ECCV'2002, volume LNCS 2350, pages 661{675, Copen-hague, June 2002.

[111] P. P�erez and J. Vermaak. Bayesian tracking with auxiliary discrete processes. application todetection and tracking of objects with occlusions. In Proc. ICCV'05 Workshop on DynamicalVision, Beijing, China, October 2005.

[112] P. P�erez and J. Vermaak. Visual tracking and auxiliary discrete processes. In Proc. Int.Symp. on Applied Stochastic Models and Data Analysis, Brest, France, May 2005.

[113] S. Prima, D.L. Arnold, and D.L. Collins. Multivariate statistics for detection of MS activ-ity in serial multimodal MR images. In Sixth International Conference on Medical ImageComputing and Computer-Assisted Intervention, MICCAI'2003, Lecture Notes in ComputerScience, Montreal, Canada, November 2003.

[114] F. Rousseau, R. Fablet, and C. Barillot. Robust statistical registration of 3d ultrasound im-ages using texture information. In IEEE Int. Conf. on Image Processing, ICIP'03, Barcelona,Spain, September 2003.

[115] F. Rousseau, P. Hellier, and C. Barillot. A fully automatic calibration procedure for freehand3d ultrasound. In IEEE Int. Symp. on Biomedical Imaging, Washington D.C., July 2002.

[116] F. Rousseau, P. Hellier, and C. Barillot. Calibration method for 3D freehand ultrasound.In Medical Image Computing and Computer Assisted Intervention, MICCAI'03, Montreal,Canada, November 2003.

[117] V. Samson and P. Bouthemy. Learning classes for video interpretation with a robust parallelclustering method. In Proc. Int. Conf. Pattern Recognition, ICPR'04, Cambridge, UK,August 2004.

[118] O. Tahri and F. Chaumette. Application of moment invariants to visual servoing. In IEEEInt. Conf. on Robotics and Automation, ICRA'03, volume 3, pages 4276{4281, Taipeh, Tai-wan, May 2003.

[119] T. Veit, F. Cao, and P. Bouthemy. Probabilistic parameter-free motion detection. In Conf.Computer Vision and Pattern Recognition, CVPR'04, Washington, DC, June 2004.

[120] Th. Veit, F. Cao, and P. Bouthemy. A maximality principle applied to a contrario motiondetection. In Proc IEEE Int. Conf. on Image Processing (ICIP'05), Genova, Italy, September2005.

[121] R. Venkatesh Babu, P. P�erez, and P. Bouthemy. Robust tracking with motion estimationand kernel-based color modelling. In Proc. IEEE Int. Conf. on Image Processing (ICIP'05),Genova, Italy, September 2005.

[122] J. Vermaak, S. Maskell, M. Briers, and P. P�erez. Bayesian visual tracking with existenceprocess. In Proc. IEEE Int. Conf. on Image Processing (ICIP'05), Genova, Italy, September2005.

[123] J. Vermaak, S. Maskell, M. Briers, and P. P�erez. Multi-target tracking and existence. InProc. Int. Conf. Information Fusion (FUSION'05), Philadelphia, USA, July 2005.

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[124] L. Xie and P. P�erez. Slightly supervised learning of part-based appearance models. In Proc.IEEE Workshop on Learning in Computer Vision and Pattern Recognition, LCVPR'04,Washington, DC, June 2004.

[125] J.-F. Yao, G. Piriou, and P. Bouthemy. A motion-based event detection method for videosequences. In Int. Conf. on Statistics, Combinatorics and Related Areas, Portland, ME,October 2003.

[126] J. Yuan, P. Ruhnau, E. M�emin, and C. Schnoerr. Discrete orthogonal decomposition andvariational uid ow estimation. In 5th Int. Conf. on Scale-Space and PDE methods inComputer Vision (Scale-Space'05), Hofgeismar, Germany, 2005.

Book chapters

[1] I. Bloch, J.-P. Le Cadre, and H. Ma�tre. Approches probabilistes et statistiques. In I. Bloch,editor, Fusion d'informations en traitement du signal et des images, Trait�e IC2, chapter 6,pages 87{118. Hermes, 2003.

[2] F. Cao, Th. Veit, and P. Bouthemy. Image comparison and motion detection by a contrariomethods. In L. Harris and M. Jenkin, editors, Computational Vision in Neural and MachineSystems. Cambridge University Press, 2005.

[3] F. Chaumette. Asservissement visuel. In W. Khalil, editor, La commande des robots manip-ulateurs, Trait�e IC2, chapter 3, pages 105{150. Herm�es, 2002.

[4] P. Gros, R. Fablet, and P. Bouthemy. New descriptors for image and video indexing. InR.C. Veltkamp, H. Burkhardt, and H.-P. Kriegel, editors, State-of-the-Art in Content-BasedImage and Video Retrieval, Computational Imaging and Vision, Vol.22, chapter 10, pages213{234. Kluwer Academic Publ. 2001.

[5] J.-P. Le Cadre. Data association and multitarget tracking. In A.K. Hyder, E. Shakbazian, andE. Waltz, editors, Multisensor Fusion, volume 70 of NATO Sciences Series, II. Mathematics,Physics, Kluwer, pages 331{351. NATO ASI on Multisensor Data Fusion, 2002.

[6] J.-P. Le Cadre, V. Nimier, and R. Reynaud. Fusion en traitement du signal. In I. Bloch,editor, Fusion d'informations en traitement du signal et des images, Trait�e IC2, chapter 2,pages 29{52. Hermes, 2003.

[7] E. Marchand and F. Chaumette. Reconstruction 3D par vision dynamique active. In M. D-home, editor, Perception visuelle par imagerie vid�eo, Trait�e IC2, chapter 6, pages 213{249.Hermes, 2003.

[8] P. Mus�e, F. Sur, F. Cao, Y. Gousseau, and J.-M. Morel. Shape recognition based on an acontrario methodology. In H. Krim and A. Yezzi, editors, Statistics and Analysis of Shapes.Springer Verlag. to appear.

Books (written)

[1] F. Cao. Geometric Curve Evolution and Image Processing. Number 1805 in Lecture Notesin Mathematics. Springer Verlag, 2003.

[2] T. Corpetti. Analyse d'�ecoulements uides �a partir de s�equences d'images. Collection Traite-ment du Signal et de l'Image. Hermes Science Publications, Lavoisier, 2004.

Technical reports

[1] P. Bouthemy, C. Hardouin, G. Piriou, and J. Yao. Auto-models with mixed states andanalysis of motion textures. Technical Report 1682, Irisa, 2005.

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[2] T. Brehard and J.-P. Le Cadre. Initialization of particle �lter and posterior cramer-rao boundfor bearings-only tracking in modi�ed polar coordinate system. Technical Report 1588, Irisa,2004.

[3] Th. Br�ehard and J.-P. Le Cadre. Closed-form posterior Cram�er-Rao bound for bearings-onlytracking. Technical Report 1701, Irisa, 2005.

[4] F. Cao. Contrast invariant, parameterless detection of good continuations, corners andterminators. Technical Report 1487, Irisa, 2002.

[5] F. Cao and P. Bouthemy. A general criterion for image similarity detection. Technical Report1732, Irisa, 2005.

[6] F. Cao, J. Delon, A. Desolneux, P. Mus�e, and F. Sur. A uni�ed framework for detectinggroups and application to shape recognition. Technical Report 1746, Irisa, 2005.

[7] F. Cao, P. Mus�e, and F. Sur. Extracting meaningful curves from images. Technical ReportRR-5067, Inria, 2003.

[8] F. Cao, P. Mus�e, and F. Sur. Extracting meaningful curves from images. Technical Report5067, Inria, 2004.

[9] A. Comport, E. Marchand, and F. Chaumette. Robust and real-time image-based trackingfor markerless augmented reality. Technical Report 1534, Irisa, 2003.

[10] I. Corouge, P. Hellier, B. Gibaud, and C. Barillot. A local approach for inter-individualfunctional registration. Technical Report 4415, Inria, 2002.

[11] F. Dambreville and J.-P. Le Cadre. Minimax optimization of continuous search e�orts forthe detection of a target. Technical Report 1403, Irisa, 2001.

[12] G. Flandin and F. Chaumette. Visual data fusion: application to objects localization andexploration. Technical Report 1394, Irisa, 2001.

[13] C. Hue, J.-P. Le Cadre, and P. P�erez. Performance analysis of two sequential monte carlomethods and posterior cramer-rao bounds for multi-target tracking. Technical Report 1457,Irisa, 2002.

[14] N. Jain, E. M�emin, and C. P�erez. Parallelization of dense uid motion estimation applicationusing openmp. Technical Report 1483, Irisa, 2002.

[15] C. Kervrann and J. Boulanger. Local adaptivity to variable smoothness for exemplar-basedimage denoising and representation. Technical Report RR-5624, Inria, 2005.

[16] A. Lehmann, P. Bouthemy, and J.-F. Yao. Comparison of video dynamic contents withoutfeature matching by using rank-tests. Technical Report RR-5586, Inria, 2005.

[17] E. Marchand and F. Chaumette. A new formulation for non-linear camera calibration usingvirtual visual servoing. Technical Report 1366, Irisa, 2001.

[18] Y. Mezouar and F. Chaumette. Path planning for robust image-based visual servoing. Tech-nical Report 1377, Irisa, 2001.

[19] P. Mus�e, F. Sur, F. Cao, Y. Gousseau, and J.-M. Morel. Accurate estimates of false alarmnumber in shape recognition. Technical Report 5086, Inria, 2004.

[20] I. Pratikakis, C. Barillot, and P. Darnault. Towards free-hand 3-d ultrasound. TechnicalReport 4399, Inria, 2002.

[21] F. Rousseau and C. Barillot. Quality assessment of electromagnetic localizers in the contextof 3d ultrasound. Technical Report 4408, Inria, 2002.

[22] O. Tahri and F. Chaumette. Determination of moment invariants and their application tovisual servoing. Technical Report 1539, Irisa, 2003.

[23] T. Veit, F. Cao, and P. Bouthemy. An a contrario framework for motion detection. TechnicalReport 5313, Inria, 2004.

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