INSIDE

Department News..........................2Research Highlights.......................6Alumni..........................................13

this issue:

CSE BYTES AND PIECESVolume 14, Fall 2007

Greetings from Lion Country! The new academic year brings fall colors and the electrifying chanting of "We arePenn State," as JoePa's kids perform their magic at Beaver Stadium. Go Lions!

The past year has been very exciting for CSE. The National Science Foundation awarded the highly selective andprestigious Career Awards to Professors Patrick McDaniel, Yuan Xie, and Sencun Zhu. Patrick Traynor, a CSEPh.D. student, won the University-wide Penn State Alumni Association Dissertation Award.

Since its inception three years ago, the Networking and Security Research Center (NSRC) has been awarded $12million in external research support. The CSE department is continuing to lead the nation in research and scholarlyactivities. As published in the Chronicles of Higher Education, faculty scholarly productivity of CSE facultyranked 3rd (computer science) and 7th (computer engineering) in the nation, respectively. I would like to congratulatethe CSE faculty, staff, students, and alumni for this outstanding achievement.

We have recruited two new faculty this year. Sean Hallgren (Ph.D.,Berkeley) joins us in the area of quantum computing. Swarat Chaudhuri(Ph.D., Penn) joins us in the area of programming languages. We missHelen DeFurio who retired after 37 years at Penn State. However, we arefortunate to have Corry Bullock as the departmental administrator.

This year, the PSES Outstanding Engineering Award went to Derek Smith,chairman and CEO of ChoicePoint.

I would like to thank Allen Puy '86 CMPSC and Lockheed Martin fordonating their tailgating spot for CSE's first Penn State football tailgateparty during the Ohio State game.

CSE at Penn State is a premier computer science and engineeringdepartment in the country. It is shaping the countries future by solvingthe critical problems confronting our nation while providing the highestquality education to our students. I am privileged and honored to beassociated with such a fine department and University.

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Helen DeFurio Retires after 37 Years ofService to Penn State

Helen DeFurio started her career at Penn State in theDepartment of Mathematics right after graduating highschool in 1970. She held various duties within thatdepartment such as receptionist, typist, secretary, budgetclerk, and senior accounting clerk. In 1983 she was hiredby Joseph M. Lambert, department head of computerscience. In 1993, the department merged to become theDepartment of Computer Science and Engineering(CSE).

Helen served as the administrative manager and depart-ment operation administrative assistant IV in CSE. Shewas responsible to the department head for effectiveadministration of the office, staffing, scheduling,supervising, and budgeting. She coordinated humanresource and administrative functions for the departmentincluding academic appointments, personnel recommen-dations, payroll, promotion and tenure procedures, spaceissues, affirmative action, research and general budgetadministration, approval of funds,security of files, and confidentialityof information.

Dr. Rangachar Kasturi said that hebegan working with Helen in 1993when the Department of ComputerScience (College of Science) wasmerged with the computerengineering program (College ofEngineering). While most of thefaculty found it hard to know therules and regulations of one college,Helen had to deal with two collegeadministrations on a daily basis. Shewas the pioneer who managed adifficult and uncertain merger withincredible efficiency and a most

DEPARTMENT NEWS

positive attitude. She was an outstanding mentor and avery good friend to the office staff. She was particularlywell skilled in balancing the workload among staff andmaintaining excellent morale. Kasturi did not hearanything but praise for her fairness and sensitivity to theneeds and concerns of her fellow staff.

In 2001 Dr. Kasturi assumed the responsibility toorganize the IEEE Computer Vision and PatternRecognition conference. At his request, Helen agreedto serve as the conference secretary and accompany himto the conference in Hawaii. All was going well and theconference committee was expecting to set a newattendance record. However the aftermath of Sept. 11caused even seasoned travelers to worry about travelingfar away from home. As someone who had not traveledfar from central Pennsylvania, it was a very difficultdecision for Helen and her family. But ultimately it washer dedication to her work that guided her decision. Shewas at the conference when a new attendance recordwas set!

Helen's talents were put to good useduring the planning stages for thenew Information Sciences andTechnology (IST) Building. Shewas making sure that the depart-ment's needs were addressed. In thebuilding committee meetings,Kasturi was always amazed at herabilities to pay attention to detailsduring the planning stages to pre-vent them from becoming problemsafter construction. Raj Acharya,department head, said that "Helenhas been instrumental in the suc-cess of the computer science andengineering department. She hasmade sure that the departmentaladministration operated smoothly.Helen Defurio and Raj Acharya

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She was indispensable during the planning of the newIST Building."

Seven department heads and 37 years later, HelenDeFurio has retired from Penn State. Her plans for thefuture include spending more time with family, garden-ing, and traveling.

Corry Bullock Hired as the New Manager ofAdministrative Operations

Corry Bullock accepted theposition of manager, admini-strative operations at CSE. Shehas been with Penn State for 22years. She has worked invarious areas within Penn Statesuch as the senior vice president

for research and graduate studies and the College of Artsand Architecture. She recently moved back to the StateCollege area after living in the Philadelphia region forfive years. Before accepting the position, she worked atPenn State Great Valley, School of Graduate ProfessionalStudies. In the fall she started a two-year program tobecome a Penn State certified research administrator.

New Faculty

Sean Hallgren received his Ph.D.from the University of California,Berkeley, and spent a year as a post-doc at the Mathematical SciencesResearch Institute in Berkeley, CA.He was then a National ScienceFoundation postdoc at Caltech fortwo years in the computer science

department and at the Institute for Quantum Information.Before joining Penn State, he ran the quantumcomputation group at NEC Laboratories in Princeton,NJ, for four years.

DEPARTMENT NEWS

Swarat Chaudhuri received his B.S.degree in computer science from theIndian Institute of Technology,Kharagpur, in 2001, and a Ph.D.degree in computer science from theUniversity of Pennsylvania in 2007.

His research interests include program analysis, formalmethods in software engineering, and applications oflogic, automata, and concurrency theory.

Padma Raghavan Appointed the FirstDirector of the Institute for ComputationalScience

Padma Raghavan, professor,has been appointed the firstdirector of the Institute forComputational Science. She isassisted by an executive com-mittee comprised of the deansof core colleges and representa-tives from participating insti-

tutes and a steering committee consisting of Universityfaculty. According to Eva J. Pell, senior vice presidentfor research and the dean of the Graduate School, "TheInstitute for Computational Science (ICS) is acollaborative effort between the academic colleges, theApplied Research Laboratory, the Office of InformationTechnology Services, and the Office of the Senior VicePresident for Research. The ICS was developed toenhance Penn State's national and international presenceand stature in computational science, to developproposals that will win new opportunities for Penn Statefaculty and students to pursue high level collaborativeinterdisciplinary research; to drive the use andunderstanding of computational science in graduateeducation, and to increase access and awareness of high-end computing facilities." For more information aboutICS, please visit their web site at: www.ics.psu.edu

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DEPARTMENT NEWS

Three CSE Faculty Receive PrestigiousNational Science Foundation CAREERAwards

With prestigious awards from the National ScienceFoundation (NSF), three CSE faculty members aretackling various issues. The researchers have earnedNSF's Faculty Early Career Development (CAREER)Award. According to NSF, they established theCAREER program in 1994 in recognition of the criticalroles played by faculty members in integrating researchand education, and in fostering the natural connectionsbetween the processes of learning and discovery. TheCAREER program is a Foundation-wide activity thatoffers NSF's most prestigious awards for junior facultymembers, which embodies NSF's commitment toencourage faculty to practice and academic institutionsto value integration of research and education.

Patrick McDaniel, associateprofessor, "CAREER: Reali-zing Practical High Assurancethrough Security-Typed Infor-mation Flow Systems."

Yuan Xie, assistant professor,"CAREER: Process VariationAware Embedded MPSoCSynthesis."

Sencun Zhu, assistantprofessor, "CAREER: Comba-ting Worm Propogation inEmergent Networks."

CSE Faculty Member Receives STOC 2007Best Paper Award

Martin Furer received the "BestPaper Award" at the Thirty-Ninth ACM Symposium onTheory of Computing (STOC2007) for the paper titled,"Faster Integer Multiplication."

He announced a new algorithm that has become a betterasymptotic complexity. To read this paper, please visitthe following web site: www.cse.psu.edu/~furer/Papers/mult.pdf. The main criterion for selection is the same asfor being a top-rated paper in the conference:introduction of a strong new technique, solution of along-standing open problem, introduction and solutionof an interesting and important new problem, etc. Theseare the characteristics associated with giving a paper thehighest score. Additionally, the committee should havesubstantial confidence of the correctness of the paper.STOC is one of the two most prestigious conferences intheoretical computer science.

Two CSE Faculty Receive InterdisciplinaryNational Science Foundation Award

Professors Robert Collins andYanxi Liu, together with R.Barry Ruback (Sociology) andChristopher Byrnes (Math/ARL) have received a grant($750,000) from the NationalScience Foundation to supportinterdisciplinary research intothe social behavior of crowds.

Their research will combine amathematical model of humancollective behavior with soft-

ware for image and texture tracking, classification,

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DEPARTMENT NEWS

simulation, and animation to test the hypothesis thatbehavior in a large crowd results primarily from socialinfluence within and between small groups ofindividuals. Empirical sociological evidence will begathered by tracking all individuals in video of crowdsof varying nature, topology, size, and density usingautomated computer vision tracking algorithms, amethodological breakthrough capable of providingquantitative characterization of real crowds faster andmore accurately than human observation. The theory andthe tools to be developed by this project can assistpersonnel in law enforcement, emergency management,and event management to minimize violence, speed upevacuations, and reduce accidental injury during largepublic gatherings.

Networking and Security Research CenterNamed a Ben Franklin Center of Excellence

The Networking and Security Research Center (NSRC)was named a Ben Franklin Center of Excellence in 2007.According to Tom La Porta, director, NSRC, "thisrelationship will help us meet one of our primary goals— fostering partnerships with industry and providingan outlet for our technology to have an impact onsociety." While the primarygoal of the Ben Franklinsupport is to assist in develop-ing ties with Pennsylvania-based companies, the NSRCcontinues to build relation-ships with companies all overthe world. According to theBen Franklin web site,"Centers of Excellence mustprovide technology transferand outreach mechanismsthat provide tangible tech-nical and educational benefitsto individual companies in

Pennsylvania, typically the center members, and facilitategeneral industry sector business stability and grown."The NSRC includes twelve faculty and approximately50 Ph.D. and M.S. students and several undergraduatehonors students. The expertise of the members includestelecommunications, mobile networking, protocoldesign, performance analysis and simulation, wirelesscommunication, networked applications, and largenetworking software systems. Security permeates all ofthese areas. The center boasts experts on telecommuni-cations security, Internet security, policy, secure operatingsystems, access controls, and cryptography.

According to La Porta, "as part of our effort to providetechnical leadership, we introduced our first short coursein spring 2007. The one-and-a-half day course onenterprise security was taught by Professor PatrickMcDaniel and was very well received. We are planningtwo more such courses in the next year." In the pasttwelve months, the members of the center havecollaborated to raise more than $4.4 million in fundingthrough grants from federal or state-based fundingagencies including several National Science Foundation(NSF) and Department of Defense grants. In the pastthree years the center has raised more than $12 million

in new funding. Two membersof the center won prestigiousNSF CAREER Awards andone received a DefenseAdvanced Research ProjectsAgency Young InvestigatorAward.

To learn more about the center,visit their web site at:

http://nsrc.cse.psu.edu

Some NSRC members with a check from theBen Franklin Technology Partners

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Figure 1: Bayesian seg-mentation of human bodypose from images is animportant step towardsbetter machine understan-ding of human form andaction.

The Laboratory for Perception, Action, and Cognition

The Laboratory for Perception, Action, and Cognition(LPAC) is directed by Professors Robert Collins andYanxi Liu. Research in the lab and the name of the labitself are motivated by multiple disciplines that must bebrought together to develop robust intelligent systems:

Perception = Computer Vision: Aristotle defined visionas the act of knowing what is where by looking.Likewise, the goal of computer vision is to interpretvisual sensor data including still imagery and video tomeasure 3D scene structure, infer the identity andlocations of objects, and to recognize dynamicallyoccurring activities and events.

Action = Robotics: By coupling computer vision inputdevices with robotic actuators, feedback loops can bebuilt that exhibit intriguing emergent behaviors. LPACtakes a broad view of the term actuator to includeunmanned vehicles, pan/tilt/zoom heads that activelychange the camera viewpoint (e.g. active vision systems),and monitor/projector systems that display usefulinformation into or even onto the scene (e.g. smartspaces).

Cognition = Artificial Intelligence: Thereal world is too complex for a completedescription to be coded by hand. The LPACgroup focuses instead on developingintelligent systems that learn from trainingexamples and unsupervised explorationusing mathematical representations thatleverage fundamental constraints present inthe physical world, whether they be theimportance of gravity in interpreting naturalimagery, the periodic nature of locomotion(e.g. gaits), or the interplay of randomnessand regularity in the appearance of naturaland man-made scenes.

Some of the research areas actively being explored bythe lab are described in this article.

Understanding Human Form and Action

Humans are inarguably the most important objects thatan intelligent system must be able to interact with. Aportion of LPAC research is devoted to developing state-of-the-art machine vision systems that can recognizepeople, describe what they are doing, and infer what theyare trying to achieve. This research proceeds on manyfronts: detecting humans and describing their body pose,biometric identification from face and gait, andsociology-based methods for understanding thecollective behavior of small groups and crowds.

Articulated Body Fitting

Our work in automated body model fitting is based onthe hypothesis that it is necessary to explicitly identifythe pose of a person over time in order to describe theirbody motions and recognize their actions and activities.Under National Science Foundation (NSF) funding, wehave developed methods for segmentation of articulated,part-based human body models from images. A Bayesian

approach is used to take advantage ofstrong priors on human body shape andmotion and to combine multiple imagecues such as color and edges in a robustfashion. The resulting method can segmentpeople with unknown body pose seen fromunknown viewpoint (an example is shownin Figure 1). Likely body poses are foundusing a hybrid search strategy that com-bines deterministic (dynamic program-ming) and stochastic (sequential MonteCarlo) techniques to automatically searchthrough a high-dimensional space ofpossible body pose configurations.

RESEARCH HIGHLIGHTS

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Biometric Identification from Face and Gait

Biometric identification research in the lab is concernedwith passive, visual methods for identification, asopposed to fingerprint analysis or identification basedon RFID tags. Face, body shape, and gait have all beendemonstrated to yield promising results.

Human facial asymmetry has long been a critical factorfor evaluation of attractiveness and expression inpsychology and anthropology, though most studies arecarried out qualitatively and using human raters. Wehave investigated the use of statistical facial asymmetrymeasurement as a biometric under expression variations.Our initial findings demonstrate that the asymmetry ofspecific facial regions capture individual differences thatare robust to variation in facial expression. Moreimportantly, our experimental results show that facialasymmetry provides discriminating power orthogonalto conventional face identification methods, and ourwork appears to be the first to show quantitatively thepower of facial asymmetry quantifications, pose-invariant human identification, identification of attractivevs. non-attractive people, gender differences, andtemporal variations during expression for emotionclassification. Our recent paper, "Measurement ofAsymmetry in Persons with Facial Paralysis," won firstplace in the clinical science category and best paperoverall at the Annual Conference of Plastic andReconstructive Surgeons.

Figure 2: Facial asymmetry has been shown to provide a human biometric that is relatively invariant todiffering facial expressions.

Human gait recognition relies on two cues to identify anindividual: the shape of their body and the way that theymove. We have developed a gait recognition method thatrepresents gait sequences as spatiotemporal "Frieze"patterns (Figure 3). This representation is intriguingbecause both shape and motion information arecombined into a single pattern where they can becompared jointly. We have recently extended thisapproach to create Shape Variation Based Frieze patternswhere the common shape is factored out and representedseparately from the shape-variation information. Thismethod has been shown to outperform several state-of-the-art gait recognition methods when tested onsequences where a person is seen carrying a box orwearing a heavy coat or backpack that changes theirsilhouette shape in each frame.

RESEARCH HIGHLIGHTS

Inner

Philtru Subj Subj

Spatiotemporal facial asymmetry of expression

V III

VII

VII I

Upper Frontal view Back view

Lower side viewLower Frontal view

Time one columnof the pattern

silhouette

histogram

Figure 3: We represent gait sequences as a spatiotemporal"Frieze" pattern where shape and motion information arecombined and can be compared jointly.

projection

Side view

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Understanding Collective Behavior

Professors Collins and Liu, together with Penn Statecollaborators from the sociology and mathematicsdepartments recently received an NSF award to supportinterdisciplinary research into the social behavior ofcrowds. This research combines a mathematical modelof human collective behavior with software for imageand texture tracking, classification, simulation, and ani-mation to test the hypothesis thatbehavior in a large crowd resultsprimarily from social influencewithin and between small groupsof individuals. Preliminary workon the grant has demonstratedautomated pedestrian detectionand tracking tools. Hierarchicalagglomerative clustering of theresulting set of time-stampedpedestrian trajectories yields in-sights into which individuals mayhave been traveling together(Figure 4). Quantitative evaluationof automatically detected smallgroups against those identified byhuman coders yielded substantial statistical agreement.This work demonstrates that empirical sociologicalevidence can be gathered by tracking all individuals invideo of crowds using automated computer visiontracking algorithms, a methodologicalbreakthrough capable of providingquantitative characterization of realcrowds faster and more accuratelythan human observation. The theoryand the tools to be developed by thisproject can assist personnel in lawenforcement, emergency manage-ment, and event management to mini-mize violence, speed up evacuations,and reduce accidental injury duringlarge public gatherings.

Figure 4: Three small groups traveling through acrowded atrium, automatically detected byhierarchical agglomerative clustering on trackedpedestrian trajectories.

Figure/Ground Separation

Research on persistent object tracking addresses theproblem of tracking objects for long periods of timeduring which the appearance of both the object and itsenvironment may change. Foreground-background (orfigure-ground) segmentation is emerging as a keyapproach to solving this problem. By explicitlyseparating object pixels from the background, a tracker

can adapt to object and back-ground appearance changesseparately, and in a principledway. Professors Collins and Liuwere the first to pose tracking asa two-class classification prob-lem, where object pixels must bediscriminated from backgroundpixels based on local image cuessuch as histograms of color ortexture. Recent work in the labexplores the use of motion infor-mation to efficiently segmentmoving objects from thebackground. The evidence accum-

ulation underlying the procedure can be viewed asspatiotemporal Markov Random Field (MRF, see Figure5) combining evidence of motion at each pixel within afixed temporal window with spatial constraints that

encourage consistency in labeling andbetween neighboring pixels. Themaximum a posteriori estimate ofmotion energy is approximated by abelief propagation procedure, imple-mented as spatial and temporalmessage passing within the MarkovRandom Field.

RESEARCH HIGHLIGHTS

Figure 5: Moving objects are segmentedfrom video sequences using beliefpropagation within a 3D spatiotemporalMarkov Random Field.

Input videos

Detection

BP in a 3D MRF

MMSEEstimate

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RESEARCH HIGHLIGHTS

Modeling while Tracking

It is common for trackers toeventually lose the object theyare tracking due to temporaryocclusion, large cameramotion, or large changes inillumination or viewing angle.To achieve persistent tracking,mechanisms must be in placeto recover from these trackingfailures. We have developeda method to model the objectadaptively and automaticallywhile tracking, so that theobject can be detected and recognized again after losingit (Figure 6). The models, which are collections of smallview-dependent intensity patch models indexed on aview sphere, are accumulated on-the-fly during thetracking run. Although we may start out with very littleinformation about an object we are tracking, the processof tracking it through multiple views quickly accumu-lates a large amount of data on its appearance andmovement. As we track the object over longer sequenceswhere viewpoint is changing, we are able to accumulateand chain together a large collection of view-specificmodels so that object appearance is represented across aprogressively larger and larger range of viewing angles.

Tracking Multiple Targets in Formation

Despite a decade of research into vision-based trackingand the commercial availability of automated videosurveillance systems, tracking identical targets information remains an unsolved problem.The problem isdifficult due to high densities of constantly moving,spatially overlapping objects, all of which look similar(e.g., members of a marching band all dressed in identicaluniforms). We hypothesize that the only way to reliablytrack a single target in the presence of nearby confusors

Figure 6: A collection of view-dependent models built on-the-fly while tracking can help a persistent tracker recover fromfailure by enabling re-detection of a lost target object.

is to simultaneously track allof them. Furthermore, whenthe objects are known to bearranged in spatial formation,we can leverage the under-lying lattice structure of theirpositions to provide statisticalinference within a Bayesiannetwork of spatial andtemporal tracking constraints.This methodology allows usto handle difficult multi-targettracking problems such astracking identical textureelements on cloth, animals in

herds, and marching individuals in the Penn State BlueBand (Figure 7). In each case, even though the "targets"are closely spaced and similar in appearance, imposingspatial neighbor constraints in the form of a 2D latticeallows the tracker to successfully disambiguate and trackeach individual in the formation.

Computational Symmetry

Symmetry is an essential and ubiquitous concept innature, science, and art. Numerous biological, natural,and man-made structures exhibit symmetries as a funda-mental design principle or as an essential functionality.Whether by evolution or by design, symmetry leads

Figure 7: Tracking a formation of individuals from thePenn State Blue Band marching during a halftime show.

ViewSphere

Modeling

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RESEARCH HIGHLIGHTS

simultaneously to an aesthetic and a practical usability,making it universally appealing. A computational modelfor symmetry is especially pertinent to robotics, computervision, and machine intelligence, in general, because inthese fields we are studying how a man-made intelligentbeing can perceive and interact with the chaotic realworld in the most effective manner. Recognition ofsymmetries is the first step towards capturing the innerstructure of a real-world prob-lem and minimizing redundan-cy, which can usually lead todrastic reductions in computa-tional requirements.

Group theory, being the mathe-matics of symmetry, placessymmetry on a sound, albeitideal, theoretical footing.Crystallographic group theorytells us that regardless of theinfinite number and variety ofFrieze and wallpaper patterndesigns in the world, there areonly 7 and 17 underlying symmetrygroups, respectively! The keychallenge is to turn the mathematicalconcepts of group theory into a compu-tationally useful tool for perception ofsymmetric patterns, despite systematicdistortions or noise in real-world data.So far, no one has shown a robust,widely applicable general symmetrydetection algorithm for real-worlddigital data (images or otherwise) inspite of many years of effort.

Professor Liu has taken the lead indefining and exploring the new sub-field of "computational symmetry."LPAC has several research efforts that

involve cross-disciplinary applications of group theoryand statistical learning theory to characterize symmetryand departures from regularity, both qualitatively andquantitatively in noisy real-world datasets.

Automatic Discovery and Analysis of Symmetry

We have developed a novel automatic lattice (trans-lational symmetry) extractionalgorithm for texture that haveboth global and local deforma-tions (Figure 8) by treatingsymmetry detection as a higher-order correspondence problem,solved using spectral methods.When combined with previouswork we have done on charac-terizing all rotation, reflection,and glide reflection symmetriesin a repeated "wallpaper"pattern, this work forms thefundamental basis for machineperception of periodic patterns

in real-world imagery.

An interesting example showing thediversity of this approach is charac-terization of the firing fields of thenewly discovered "grid cells" foundin the rat dorsolateral medical entor-hinal cortex (Figure 9). As it turns out,the firing fields present hexagonalstructured patterns that have asymmetry group of "P6M" (one of the17 wallpaper groups) clusteringaround two types of regularities! Thisrecent result of ours was published inthe Computational NeurosciencesJournal (May 2007). As part of ourwork, we have created a publicly

Figure 8: Examples of automatically detected deformedlattice structures from input photos.

Figure 9: The firing fields of grid cellsfound in the rat dorsolateral medialentorhinal context (left) and theautomatically detected nearest"unwarped" regular lattice (right).

Original Regularized

Cell t1c1

Type II

Cell t5c3

Type III

Cell t7c4

Type III

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accessible Near-Regular Texture (NRT) database tofacilitate quantitative assessment of computationalmethods for detecting periodic and symmetric patterns(http://vivid.cse.psu.edu/texturedb/gallery/). At this sitewe maintain: (1) the first systematic collection of regularand NRT patterns; (2) comparison results on state-of-the-art texture synthesis and analysis algorithms; and (3)NRT lattice detection benchmarks and results. This PennState NRT database is now gaining wider usage, recog-nition, and contributions from the graphics and visionresearch community.

Near-Regular Texture Applications in Graphics andVideo

Discovery of near-regular texture in photos and videoleads to exciting graphics applications. One example istexture replacement where analysis of a repeated tex-ture in one photo allows discovery and characterizationof surface material shape and scene lighting, andultimately, replacement of the original texture with a newone (Figure 10).

RESEARCH HIGHLIGHTS

Figure 11: Texture replacement of a dynamic near-regular texturein video.

Figure 10: Synthesis and texture-replacement results from inputphotos (far left in each row) by analyzing geometry and lightingdeformation fields, which allows replacement textures to be overlaidon the image as if they were painted on a real surface (top row), ornew surfaces to be generated and retextured using lighting andgeometry effects learned from the input image sample (bottom row).

In addition to static textures in the photo, we have alsodemonstrated analysis of dynamic near-regular texturein video sequences. For example, Figure 11 shows oneframe of results of replacing textured cloth in a moviewith another texture, a feat that is only possible due toour ability to precisely track repeated textures in video.

Our texture tracking algorithms can successfully trackmoving clothes with occlusion, distorted patterns underdisturbed water, and even crowds of people in motion(e.g., the marching band example presented previously).Quantitative evaluations show that our lattice-basedMarkov Random Field model for dynamic NRT trackingoutperforms existing tracking algorithms on challengingtexture tracking tasks.

Machine Learning-based Biomedical Image Analysis

LPAC research into biomedical image analysis has thelong-term objective of building a computational frame-work for automatic disease classification, discrimination,and prediction. We take an image feature-based statisticalmultivariate machine learning approach on multimodalbiomedical images including, but not limited to, highresolution magnetic resonance images (MRI), CTimages, multispectral microscopic images, and opticalphotos and videos. Working closely with medicalresearchers and clinicians, we have studied a wide range

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Figure 13: Learning a discriminative feature space for computer aided diagnosis of central nervoussystems diseases such as schizophrenia and Alzheimer's.

Figure 12: Multispectral thin Pap smear image analysis to detect cancer cells.

RESEARCH HIGHLIGHTS

Background Segmentation

Intensity Normalization

Feature Extraction

Feature Screening

Classification

Candidate Region Detection

Region Merging

1. ImagePreprocessing

2. PixelClassification

3. RegionDetection

Cancerous Regions

Multispectral Pap Smear Images

Input Image DiscriminativeFeature Space

Automatically learnedDiscriminativeanatomical locations

of applications with one focusedgoal: discovering discriminativefeature subspaces that lead toautomatic object semantic classprediction. Our work covers thedevelopment of computer algo-rithms for learning-based defor-mable registration, atlas-basedsegmentation, 3D shape represen-tation and analysis, innovativeimage feature extraction anddiscriminative feature subspaceinduction and selection. We haveapplied our methods success-fully in CT neural images fordiscriminating among normal,infarct, and blood cases forimage content-based retrievalfrom large, multimedia strokepatient databases, and tohyperspectral Pap smearmicroscopic images forscreening cancer cells fromnormal cells (Figure 12). Onecurrent challenging andexciting project involves automatic classification andprediction of neuropsychiatric central nervous systemsdiseases (Figure 13) such as schizophrenia andAlzheimer's disease from high resolution MRIs.

Summary

Professors Collins and Liu bring more than 40 years ofcombined experience in vision, robotics, and graphicsto their role as co-directors of LPAC. Their years ofsuccessful research funded by NSF, the National Instituteof Health, The Defense Advanced Research ProjectsAgency, the Pennsylvania Health Department, and the

University of Pittsburgh Medical Center have led toexciting applications of computer vision to problems inhuman identification, object tracking, pattern analysis,and medical diagnosis. They continue to enthusiasticallyembrace multi-disciplinary collaboration with computerscientists, mathematicians, statisticians, sociologists,neuroradiologists, oncologists, and biomedical engineersto find new applications for the computer vision tech-nology developed in LPAC.

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ALUMNI

Alumni Update

The following alumni information has been compiled from the recent graduate responses and from the onlinealumni questionnaire (www.cse.psu.edu/info/newsletter/questionnaire.php). I want to encourage you to respond toone of these formats if you have not done so in the recent past so that we can provide up-to-date information to ourreaders and so that we can facilitate contact among our alumni. For this reason, we ask that you check the e-mailaddress carefully; if you find an error, please send the correction/change to the editor (jenny@cse.psu.edu). If youre-mail address is handwritten, please remember that some addresses are case-sensitive, so do not mix and matchyour letters! You can also send updates directly to the editor via e-mail. Please be sure to reference the newsletterand affirm your willingness to have the information published. You may have already sent information to thedepartment, but if you did not check the "yes" box or indicate your permission for inclusion in the newsletter, wecannot print it. This newsletter can be viewed online at: www.cse.psu.edu/info/newsletter/volume14/Volume14.pdf.

1969

John Mashey. Consultant, Techviser, Portola Valley, CA.He is a consultant for Venture Capitalist and technologycompanies. He is also a trustee at the Computer HistoryMuseum. (mash@heymash.com)

1970

Stephen Knapp. Project scheduler, American ElectricPower. His duties include planning and scheduling ofengineering, procurement, and construction of environ-mental upgrades on a coal-fired, electrical generationplant. (awsedr5@aol.com)

1971

Arvid Martin. Retired from General Motors Corp. in2004. He is a self-employed realtor. (arvidlim@aol. com)

1980

Alistair Edwards. Senior lecturer, University of York,Department of Computer Science, Heslington, York,United Kingdom. He is responsible for teaching. Hisresearch involves human-computer interaction,particularly novel forms of interaction such as speechand non-speech sounds. Much of his work is centeredon the needs of users with disabilities. His homepage is:

www-users.cs.york.ac.uk/~alistair. (alistair@cs.york.ac.uk)

1982

Scott Dudley. Department manager, systems engi-neering, integration and test for Raytheon, Marlborough,MA. (scott_f_dudley@raytheon.com)

1985

Isaac Kunkel. Software development manager, BlueCross and Blue Shield of North Carolina. He is marriedto Ellen Schneeberger (QBA '88). (ikunkel@nc.rr.com)

1992

William A. Bralick, Jr. Vice president, STX Cadware,Inc., Irving, TX. His duties include overseeing all opera-tions and engineering. This company is responsible forbuilding productivity and collaboration tools for Cadenceenvironments as well as autogenerators for test-chiplayouts. (drwilltx@gmail.com)

1994

Joseph A. Bruce, Jr. Principal consultant, RABA Center,SRA International, Columbia, MD. He is responsible for

14 www.cse.psu.edu

ALUMNI

embedded systems design, software reverse engineer-ing, and software team leadership. (Joe.Bruce.PSU@gmail.com)

1995

Teebu Philip. Web systems administrator, TracfoneWireless, Miami, FL. (teebu@yahoo.com)

1999

Gandhi Thirugnanam. (gxt114@yahoo.com)

2002

Anu Vijayamohan. Consultant, Appian, Vienna, VA.

2003

Christopher White. (chris@white-smith.net)

2006

Paul L. Bard III. Customer support engineer II, CiscoSystems. He is responsible for solving advancednetworking problems with IT professionals working inhigh revenue companies. (pbardiii@cisco.com)

Alan Ding. Software engineer, Northrop GrummanCorporation, Linthicum, MD. He is involved in asystems integration effort for the U.S. Army's AidedTarget Recognition Program on unmanned aerialvehicles.

Jamie Knapil. Software engineer, Remcom, Inc., StateCollege, PA. She is responsible for developing softwarefor the government. (JamieMKnapil@gmail.com)

Tyler Lacock. Software engineer, IBM, Durham, NC.He is responsible for developing service-orientedarchitecture demonstrations.

Teofil Rus. Systems engineer, Vanguard Group. He isresponsible for application development. (tpr131@psu.edu)

Burnett Smith. Software engineer, IBM, Durham, NC.(BurnettPSmith@gmail.com)

2007

Artem Airaburg. Graduate student, Georgia Institute ofTechnology, Atlanta, GA. (artemd@gmail.com)

Meghan E. Daley. NASA/Johnson Space Center,Seabrook, TX. (daley.meghan@gmail.com)

Kit Klein. Computer engineer, Ingersoll Rand, Campbell,CA. Responsible for firmware and application develop-ment, control system design, and involved in therotational program. (KKleingsxr@gmail.com)

Eric Menendez. Graduate student, electrical andcomputer engineering, Carnegie Mellon University,Pittsburgh, PA. (eric@menendezfamily.com)

Christopher R. Patton. Software engineer associate,Lockheed Martin Corporation, King of Prussia, PA.

Stephen Tomko. Electrical engineer I, HarrisCorporation, Melbourne, FL. (sjt155@gmail.com)

Jeremy Wayne Trimble. Software engineer, Argon ST.

Daryl Wiest. Software engineer, Raytheon, StateCollege, PA. (daryl@wiest@gamil.com)

The Helping Hands of Alumni and Friends

Alumni and friends continue to actively support the CSEdepartment. We are grateful for this support. Donor-designated funds are essential if our efforts to providethe best possible environment for our students and facultyare to succeed. We encourage our alumni to designatetheir gifts for use in this department. Thank you againfor your gracious donations!

15www.cse.psu.edu

ALUMNI

This publication is available in alternative media on request.

The Pennsylvania State University is committed to the policy thatall persons shall have equal access to programs, facilities, admission,and employment without regard to personal characteristics not relatedto ability, performance, or qualifications as determined by Universitypolicy or by state or federal authorities. It is the policy of theUniversity to maintain an academic and work environment free ofdiscrimination, including harassment. The Pennsylvania StateUniversity prohibits discrimination and harassment against anyperson because of age, ancestry, color, disability or handicap, nationalorigin, race, religious creed, sex, sexual orientation, or veteran status.Discrimination or harassment against faculty, staff, or students willnot be tolerated at The Pennsylvania State University. Direct allinquiries regarding the nondiscrimination policy to the AffirmativeAction Director, The Pennsylvania State University, 328 BouckeBuilding, University Park, PA 16802-5901, Tel (814) 865-4700/V,(814) 863-1150/TTY.

Designed, produced, and edited by Jenny Latchford, CSE departmentwith support from CSE faculty. Printed/produced at Penn StateMultimedia and Print Center.

U.Ed. ENG 08-33

Alumni Questionnaire

We would appreciate an update on your activities bothprofessional and personal. We are always interested inwhat our alumni are up to since leaving Penn State! Foryour convenience, the questionnaire is online at:

www.cse.psu.edu/info/newsletter/questionnaire.php

Outstanding Engineering Alumnus

Derek V. Smith was one of therecipients of the OutstandingEngineering Alumni award in2007. A high school independentstudy program piqued Smith'sinterest in computer science. "Itaught myself how to programcomputers. That was back whenthere were paper ribbon tapes wehad to take out of the com-

puters," he chuckles.

Smith originally attended the Georgia Institute ofTechnology on a football scholarship. After his freshmanyear, he transferred to Penn State for its combination ofacademics and athletics. He earned a bachelor's degreein computer science in 1977. He returned to GeorgiaTech where he received his master's degree in businessfinance in 1979.

Smith was hired by Arthur Anderson (now Accenture)in its consulting division in Atlanta, GA. He workedthere for two years before joining Equifax in 1981 asassistant vice president for cash management andbanking relations. He rose to become executive assistantto the CEO; president of EMS, a marketing servicessubsidiary; head of credit bureau national expansion andsales; corporate treasurer; corporate CFO; and thenultimately was named Group Executive for the InsuranceServices Business segment that later becameChoicePoint, a leading provider of decision-makinginformation and technology that helps reduce fraud andmitigate risk.

In August 1997, ChoicePoint spun off into a separatepublic corporation with a specific mission. "Our goal isto demonstrate the responsible use of information whichcan help organizations and government make betterdecisions that will benefit us as consumers and societyat large," says Smith.

ChoicePoint played a role in many important, recentevents including DNA identification of victims of theWorld Trade Center attacks, assisting the Maryland StatePolice in the capture of the D.C. snipers, and the safereturn of more than 800 missing and exploited children.

In 2006, the company generated more than $1 billion inrevenue. Last year, ChoicePoint was named among theworld's top technology providers to the FinancialServices industry as part of the 2006 FinTech 100 listfor the third straight year. Today ChoicePoint employsapproximately 5,500 people in nearly 60 locations.

Smith is also the author of two books on privacy, "TheRisk Revolution: Threats Facing America andTechnology's Promise for a Safer Tomorrow" and "ASurvival Guide in the Information Age." He states, "Ihoped these books would broaden the national dialogueon protecting society while preserving individual rightsto privacy."

Nonprofit Org.U.S. PostagePAIDState College, PAPermit No. 1

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERINGTHE PENNSYLVANIA STATE UNIVERSITY111 INFORMATION SCIENCES AND TECHNOLOGY BUILDINGUNIVERSITY PARK PA 16802-6822