1 kostadin n. koruchev universidad autónoma de madrid, spain e-mail: [email protected]
TRANSCRIPT
The talk
Presentation of my university
Figure design for coding with orientation.
Brief presentation of the main themes of my research.
Universidad Autónoma de Madrid (UAM)
Located near Madrid. The official university of Madrid autonomous area.
www.uam.es
Universidad Autónoma de Madrid (UAM)
Universidad Autónoma de Madrid is one of the top-ranked Spanish Higher Education institutions.
It has 94 Ph.D. programs and 72 master’s programs
Over 32,000 students and 2,200 faculty Its campus is located 15 km (10 miles) north of Madrid’s
center and it is comfortably reachable by public transportation http://www.uam.es/presentacion/campus/
The university hospital La Paz is the biggest in the Madrid area.
www.uam.es
Escuela Politecnica Superior(Computer Science and Telecommunications Faculty)
New and dynamics faculty, founded 1993150 researchers from which
about 60 permanent staff (equiv. to prof. in Japan).
About 1500 undergraduate students.
150 graduated students.
www.ii.uam.es
Figure design for coding with orientation
The problem – to find figures suitable to code information in machine readable way, but hardly noticeable to humans.
Why? Interior design.
Machine readable orientation.Visible by humans – must be acceptable as esthetics.
Printed material Small markers, hardly visible for the humans that can
intermix with the printing (CLUSPY).
Previous works
S. Nashizaka, T. Tanikawa, IEEE VR’07, ACM VRST 09,
Use of markers that are selected by the user: Information coded by
the rotational angle.Very general figures.Using p-type
Fourier transform.
Previous work
Results: S. Nashizaka, T. Tanikawa, IEEE VR’07, ACM VRST 09, Results: Dependent on the figures. From 85% to 95% correct
determination.
Previous work
Kato and Kanev, 12th International Conference on Humans and Computers, December 7-10, 2009
Selecting the figures one can achieve better results. One do not need markers.
Predominant orientation is enough. Argue that L-like shape figures will work well.
Any L-like figure will work well. Recognition – work in progress.
Requires combinatorial algorithms (convex hull). Mix with CLUSPY – extremely sparse coding. This work is prolongation of this ideas.
General considerations
Acceptable for the humans (artist), Machine readable (formal math criteria). Efficiency.
Characteristics that can carry information: Position – OK but only relative position. Size – Depends on the distance, uniformity. Form – well known problems of image recognition. Orientation – Yes! Also mirror symmetry (left/right variants).
Not every figure can carry this information: Symmetries. We ought to cofactor any symmetry.
Least symmetric better.
},{},,,,,,{ RLZZMMLnS yx
},,{},,,,,,,{ mod NanRLZZMMLnS yx
Proposed solution:
Use the moments of the figure. They are easy to compute, reliable up to order 4-5 for usual figure sizes and are noise resistant.
-- scale and translation invariants.Easy transformable by rotation (tensors).
2/)2(
,
,
,
,
/),()()(
/),(
/),(
),(
mk
yx
my
kxkm
yxy
yxx
yx
AyxfMyMxM
AyxfyM
AyxfxM
yxfA
2mk
Rotation
Fixing the rotation:Rotating the figure among its axes of the ellipse
, makingThe angle of rotation is
Conditions the angle to be defined:
Practical requirement:
021120 ,, MMM .011 M
0220 MM
).36.0(/, 120020220 CMMMM
2/),2(atan 0220112 MMM
Further requirements
Recall:
(determines but
Determines uniquely.
Determines Z uniquely.
.actually,0 23030 CMM
403321 ||or|| CMCM
./ 12002 CMM },,{},,,,,,,{ mod NanRLZZMMLnS yx
0mod 180
Classes of figures:
Too round:
Too symmetric by X
Too symmetric by Y:
All criteria satisfied:L shapes are in.
./ 12002 CMM
.230 CM
403321 and CMCM
Recognition
The reverse problem must be solved.Precision of the discrete parameters –
absolute.Precision of the angle:
Less then 1 deg. error by pictures of size 200 x 200 pixels.N=50.
Size 40x40 less then 1.3 deg. N=400. We can encode >6 bits per figure.
Biomedical Applications and Biomedical on-line Processing. The importance of the problem:
According to IBM GTO 2010 Bioinformatics is one of the main technological areas for the next 5 years.
The state of art of the automation in the hospitals. Disconnected autonomous devices. Disjoint databases. (patients, hospitals, health insurance providers)
A lot of potential in the integration of these data. A lot of value for the patients.
Especially integration and distributed processing of the on-line data can give significant advantages to the patients.
Specific area
Biomedical monitoring application. Problems – the different modalities of the monitoring are
not integrated in the automatic systems Neurology – epilepsy.
Selected because the video data is the most demanding data-stream that we found.
Normally the physiological data are observed in periods of several seconds.
ECU, Preoperative observation, Pseudo epilepsy.
The Problem
State of art
Build a model and detect the pattern37% (at most) of the seizures are detectable. In practice some 15%.40% of the cases not detected in ICU have fatal
exit. It is clear that better detection can help.The problem – the model is not complete.Our approach – find the part that do not
conform the “normal” model.
Proposed Solution
Main problem – having just EEG there are many false alarms (3-4 times more).
Analysis of the problemHuman experts use the variety of signals – EEG,
Video, EMG, etc. to detect the situation of epileptic seizure.
The detection should be multimodal on-line and independent of details.Novelty detection.Specific seizure detection.
The novelty detector – the most peculiar partIt is that carries the maximum
informationIt is that do not confirm any
previously known modelUnique (or rare).
[K.K.&E.K. IWCIA 08], images
[K.K et al. ISVC 09] – EEG, video
Novelty detector The most peculiar part can be mathematically defined. The exact solution is combinatorial problem and the time is
not affordable. It is a problem defined in space with dimension several 1000.
In probabilistic terms it can be solved in time proportional to the data volume. We use random projection in order to solve it. Close to PCA. The probabilistic solution is feasible for all signals with exponential
decay distribution longer tail.
IMAGE-VIDEO EEG/EMG but… ECG
Solved Problems Epileptic patients in the ECU.
This is live saving technique. Epileptic patients in preoperative observation (holter).
The efficiency is much higher. Search in vary large databases of images. [KK. Pat.Rec. 08]
A single Rx unit produce some 5 images per minute, some 1200000 per year.
The most peculiar part can be conditioned – most peculiar regarding these samples – the search is very efficient.
Trying to mix these techniques with the modern bag of words. Currently bag of words can achieve good performance in the range of
up to 3000 images. The mix can solve a lot of problems.
Novelty detector – Gaussian projections
The signals with autonomous regulating systems (cardio activity, blood pressure, corporal temperature, glycemia level can not be treated that way.
Compression codes – compress the signal with for example wavelets and use the compressing components. They have exponential tail distribution.
These signals are important – the deviation in its complexity has high predictive value for different pathologies. Example – body temperature, glycemia level. To appear [M. Varela, K.K., BioSignals]
Open problems
Evoked potentials by epilepsy observation. The method of EP:
There is a signal provoked by some stimulus. The signal s(t) is smaller than the rest of EEG, that is regarded as noise n(t). The observation
u(t)=n(t)+s(t). The signal is extracted by averaging various instances of the observation.
EP(t)=Si ui(t)/N.
Example: The problem – the patient can not say his name. We do not know where the fault is.
Reception (auditory) Recognition (associative, auditory). Conscience MotorEP can give this information.EP standard procedure is unacceptable inEpileptic patients.
Solution – use the “natural” stimuli. At the moment – only sound due to the
time resolution. Observables – before and after the crisis
P1, N1, P2.
Thanks
ありがとう
6 The problem
Orientation – the main component. 10 deg. precision more than 5 bits.
Useful for a wide range of figures. The features are generic.
Easy to decode. (segmentation, extract the features, calculate the code). HUD – up to 0.6s, Computer vision – 1/30s.
Find the figures that has detectable components of S. Find formal criteria to distinguish these figures.
8 Rotation invariants
Hu invariants. Useful to detect the figure up to 8-9 figures. No information precisely about S.
Successive approximations.First moments – dot or disk.Second moments – ellipse.Next -- Legendre Polynomials – like quantum
orbital moments.
13 Can it be decoded?
CLUSPY like encoding with no marker. 120 quantization of the angle. Random angles. Save the angles for the decoding.
Segment (image processing). Calculate the moments. Calculate: ,,, yx MMA
14 Can it be decoded?
Calculate the rotation angle of the grid. Take the central element as a reference. Calculate all angle relative to that angle. Go trough the points forming spiral. Write the closest approximation to 12 deg. step.
84.0423 142.8450 347.2797 153.7838 203.5967 192.0838 …
0, 2,16, 26, 5, 22, 28,18, 22,16, -1, 3, 28
Decode by looking in the tablegenerated while printing.
Necessary number of figures 5. Complexity – except segmentation
proportional to the number of pixels.