1. 2/14/00 Computer Vision

2. 2/14/00 Computer Vision Lecturer: Ir. Resmana Lim, M.Eng. Email: resmana@petra.ac.id Text: 1) Computer Vision -- A Modern Approach by Forsyth+Ponce, 2) Misc. Papers

3. 2/14/00 Computer Vision What is Computer Vision? Input: Image / Video  Output: Description of the world

4. 2/14/00 Computer Vision What is Computer Vision? Input: Image / Video  Output: Description of the world Goals: Science: Modeling Human Vision Engineering: Model Extraction AI / HCI: Recognition

5. 2/14/00 Computer Vision What is Computer Vision? Input: Image / Video  Output: Description of the world Goals: Science: Modeling Human Vision Engineering: Model Extraction AI / HCI: Recognition Current State: A few problems solved

6. 2/14/00 Computer Vision Why is Computer Vision hard? Solving the Vision Problem = Understanding Human Brain Requires Background & Research in: - Optics - Geometry - Analysis / Linear Algebra / other Math - Statistics - Numerical Computation - Signal Processing - Physics - Biology - Psychology - Computer Hacking - etc

7. 2/14/00 Computer Vision Syllabus: Filters and Features Optical Flow Structure from Motion Density Estimation / Feature Models / PCA Tracking Object Recognition Applications / Review

8. 2/14/00 Computer Vision Syllabus: Filters and Features Optical Flow Stereo Projective Geometry / Stereo Reconstruction Structure from Motion Density Estimation / Feature Models / PCA Tracking Recognition & Perceptual Organization Image-Based Modeling (and Rendering) Applications / Review

9. 2/14/00 Computer Vision

10. 2/14/00 Examples: Visual Cortex Hubel

11. 2/14/00 Examples: Receptive Fields Hubel

12. 2/14/00 Examples: Receptive Fields Hancock et al: The principal components of natural images Data rotated Data scaled

13. 2/14/00 Computer Vision Syllabus: Filters and Features Optical Flow Stereo Projective Geometry / Stereo Reconstruction Structure from Motion Density Estimation / Feature Models / PCA Tracking Recognition & Perceptual Organization Image-Based Modeling (and Rendering) Applications / Review

14. 2/14/00 Multivariate Normal Distribution

15. 2/14/00 Bayes Decision Theory 1st Concept: Priors a a b a b a a b a b a a a a b a a b a a b a a a a b b a b a b a a b a a P(a)=0.75 P(b)=0.25 ?

16. 2/14/00 Bayes Decision Theory 2nd Concept: Conditional Probability # black pixel

17. 2/14/00 Thomas Bayes (c. 1702-1761) “Bayesians”

18. 2/14/00 Non-Bayesians... From: Numerical Recipes in C

19. 2/14/00 Bayesian Probabilities Why is such a big deal ? Snake Tracking Snake Tracking Why is such a big deal ? Snake Tracking Snake Tracking E +  ln p(x|c) + ln p(c)

20. 2/14/00 Birchfeld Histograms

21. 2/14/00 Chicken and Egg Problem: x p(x) 1 1 1111 1 2 2 2222 2 y Assume we know Max.Likelihood for Gaussian #1 Max.Likelihood for Gaussian #2

22. 2/14/00 Isodata: Some problems

23. 2/14/00 Mixture of Gaussians:

24. 2/14/00 Carson et al

25. 2/14/00 EM Examples Color Segmentation

26. 2/14/00 EM Examples Layered Motion Yair Weiss

27. 2/14/00 Linear Subspace:

28. 2/14/00 Data: PCA New Basis Vectors Kirby et al

29. 2/14/00 Cootes et al

30. 2/14/00 Condensation: Isard and Blake

31. 2/14/00 Unified View Regularization Kalman Filters Multiple Hypothesis Tracking Bayesian Belief Networks Hidden Markov Models Particle Filters Markov Random Fields

32. 2/14/00 Kinematic Models E(V) VV Constrain - Analytically derived: Affine, 3D Model, Twist/Exponential Map Learned: Linear/non-linear Sub-Spaces

33. 2/14/00 Kinematic Models Optical Flow/Feature tracking: no constraints Layered Motion: rigid constraints Articulated: kinematic chain constraints Nonrigid: implicit / learned constraints

34. 2/14/00 Overview: (Semi) Linear Models LDA PCA SLPICA Model Max. Fisher Min. Error Max. Variance Max. Entropy

35. 2/14/00 Discriminant functions: equal priors + cov: Mahalanobis dist.

36. 2/14/00 Example: Eigenfaces vs Fisherfaces Belhumer et al

37. 2/14/00 Real-world applications Osuna et al:

38. 2/14/00 Real-world applications Osuna et al:

39. 2/14/00 Comparisons: LeCun et al Paper LeCun et al

40. 2/14/00 Probabilistic Models / Bayesian Techniques Image Sensors High-Level Categories “Carl Lewis Sprint” Kinematic Model Constraints Dynamical Models Composition “Movemes”

41. 2/14/00 Gestalt Psychologists Wertheimer Kantizsa Square

42. 2/14/00 Vision Techniques for Recognition Image Video Classification

43. 2/14/00 Vision Techniques for Synthesis Image Video Image Video

44. 2/14/00 Vision Techniques for Graphics Debevec et al.Manex

45. 2/14/00 Motion Capture Popovic et al Digital Domain Kanade et al

46. 2/14/00 Motion Capture: Rebecca Allen / Twyla Tharp: The Catherine Wheel Paul Kaiser / Merce Cunningham:  “Biped”