Kostadin N. Koruchev Universidad Autónoma de Madrid, Spain e-mail: k.koroutchev@uam.es

1. Kostadin N. Koruchev Universidad Autónoma de Madrid, Spain e-mail: k.koroutchev@uam.es

2. The talk • Presentation of my university • Figure design for coding with orientation. • Brief presentation of the main themes of my research.

3. Universidad Autónoma de Madrid (UAM) • Located near Madrid. The official university of Madrid autonomous area. www.uam.es

4. Universidad Autónoma de Madrid (UAM) • Universidad Autónoma de Madrid is one of thetop-rankedSpanish 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

5. Escuela Politecnica Superior(Computer Science and Telecommunications Faculty) • New and dynamics faculty, founded 1993 • 150 researchers from which about 60 permanent staff (equiv. to prof. in Japan). • About 1500 undergraduate students. • 150 graduated students. www.ii.uam.es

6. 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).

7. 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.

8. Previous work • Results: S. Nashizaka, T. Tanikawa, IEEE VR’07, ACM VRST 09, Results: • Dependent on the figures. • From 85% to 95% correctdetermination.

9. 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.

10. 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.

11. 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).

12. Rotation • Fixing the rotation: • Rotating the figure among its axes of the ellipse , making • The angle of rotation is • Conditions the angle to be defined: • Practical requirement:

13. Further requirements • Recall: (determines but Determines uniquely. Determines Z uniquely.

14. Classes of figures: • Too round: • Too symmetric by X • Too symmetric by Y: • All criteria satisfied: • L shapes are in.

15. 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.

16. 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.

17. 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.

18. The Problem

19. State of art • Build a model and detect the pattern • 37% (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.

20. Proposed Solution • Main problem – having just EEG there are many false alarms (3-4 times more). • Analysis of the problem • Human 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.

21. The novelty detector – the most peculiar part • It is that carries the maximum information • It is that do not confirm any previously known model • Unique (or rare). [K.K.&E.K. IWCIA 08], images [K.K et al. ISVC 09] – EEG, video

22. 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

23. 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.

24. 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]

25. 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 • Motor EP can give this information. EP standard procedure is unacceptable in Epileptic patients. • Solution – use the “natural” stimuli. • At the moment – only sound due to thetime resolution. • Observables – before and after the crisis P1, N1, P2.

26. Thanks ありがとう

27. 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.

28. 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.

29. 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:

30. 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.