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SFM under orthographic projection. Trick Choose scene origin to be centroid of 3D points Choose image origins to be centroid of 2D points Allows us to drop the camera translation:. orthographic projection matrix. 3D scene point. image offset. 2D image point. I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
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1. SFM under orthographic projection • Trick • Choose scene origin to be centroid of 3D points • Choose image origins to be centroid of 2D points • Allows us to drop the camera translation: orthographic projection matrix 3D scene point image offset 2D image point

2. projection of n features in m images W measurement M motion S shape Key Observation: rank(W) <= 3 factorization (Tomasi & Kanade) projection of n features in one image:

3. solve for known • Factorization Technique • W is at most rank 3 (assuming no noise) • We can use singular value decomposition to factor W: • S’ differs from S by a linear transformation A: • Solve for A by enforcing metric constraints on M Factorization

4. Trick (not in original Tomasi/Kanade paper, but in followup work) • Constraints are linear in AAT : • Solve for G first by writing equations for every Pi in M • Then G = AAT by SVD Metric constraints • Orthographic Camera • Rows of P are orthonormal: • Enforcing “Metric” Constraints • Compute A such that rows of M have these properties

5. Results

6. Extensions to factorization methods • Paraperspective [Poelman & Kanade, PAMI 97] • Sequential Factorization [Morita & Kanade, PAMI 97] • Factorization under perspective [Christy & Horaud, PAMI 96] [Sturm & Triggs, ECCV 96] • Factorization with Uncertainty [Anandan & Irani, IJCV 2002]

8. Structure from motion • How many points do we need to match? • 2 frames: (R,t): 5 dof + 3n point locations  4n point measurements  n 5 • k frames: 6(k–1)-1 + 3n  2kn • always want to use many more CSE 576 (Spring 2005): Computer Vision

9. Bundle Adjustment • What makes this non-linear minimization hard? • many more parameters: potentially slow • poorer conditioning (high correlation) • potentially lots of outliers CSE 576 (Spring 2005): Computer Vision

10. Lots of parameters: sparsity • Only a few entries in Jacobian are non-zero CSE 576 (Spring 2005): Computer Vision

11. Robust error models • Outlier rejection • use robust penalty appliedto each set of jointmeasurements • for extremely bad data, use random sampling [RANSAC, Fischler & Bolles, CACM’81] CSE 576 (Spring 2005): Computer Vision

12. Structure from motion: limitations • Very difficult to reliably estimate metricstructure and motion unless: • large (x or y) rotation or • large field of view and depth variation • Camera calibration important for Euclidean reconstructions • Need good feature tracker • Lens distortion CSE 576 (Spring 2005): Computer Vision

13. Issues in SFM • Track lifetime • Nonlinear lens distortion • Prior knowledge and scene constraints • Multiple motions

14. Track lifetime every 50th frame of a 800-frame sequence

16. Track lifetime track length histogram

17. Nonlinear lens distortion

18. Nonlinear lens distortion effect of lens distortion

19. Prior knowledge and scene constraints add a constraint that several lines are parallel

20. Prior knowledge and scene constraints add a constraint that it is a turntable sequence

21. Applications of Structure from Motion

22. Jurassic park

23. PhotoSynth http://labs.live.com/photosynth/

24. So far focused on 3D modeling • Multi-Frame Structure from Motion: • Multi-View Stereo Unknown camera viewpoints

25. Next • Recognition

26. Today • Recognition

27. Recognition problems • What is it? • Object detection • Who is it? • Recognizing identity • What are they doing? • Activities • All of these are classification problems • Choose one class from a list of possible candidates

28. How do human do recognition? • We don’t completely know yet • But we have some experimental observations.

29. Observation 1:

30. Observation 1: The “Margaret Thatcher Illusion”, by Peter Thompson

31. Observation 1: • http://www.wjh.harvard.edu/~lombrozo/home/illusions/thatcher.html#bottom • Human process up-side-down images separately The “Margaret Thatcher Illusion”, by Peter Thompson

32. Observation 2: Kevin Costner Jim Carrey • High frequency information is not enough

33. Observation 3:

34. Observation 3: • Negative contrast is difficult

35. Observation 4: • Image Warping is OK

36. The list goes on • Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About http://web.mit.edu/bcs/sinha/papers/19results_sinha_etal.pdf

37. Face detection • How to tell if a face is present?

38. One simple method: skin detection • Skin pixels have a distinctive range of colors • Corresponds to region(s) in RGB color space • for visualization, only R and G components are shown above skin • Skin classifier • A pixel X = (R,G,B) is skin if it is in the skin region • But how to find this region?

39. Skin classifier • Given X = (R,G,B): how to determine if it is skin or not? Skin detection • Learn the skin region from examples • Manually label pixels in one or more “training images” as skin or not skin • Plot the training data in RGB space • skin pixels shown in orange, non-skin pixels shown in blue • some skin pixels may be outside the region, non-skin pixels inside. Why?

40. Skin classification techniques • Skin classifier • Given X = (R,G,B): how to determine if it is skin or not? • Nearest neighbor • find labeled pixel closest to X • choose the label for that pixel • Data modeling • fit a model (curve, surface, or volume) to each class • Probabilistic data modeling • fit a probability model to each class

41. Probability • Basic probability • X is a random variable • P(X) is the probability that X achieves a certain value • or • Conditional probability: P(X | Y) • probability of X given that we already know Y • called a PDF • probability distribution/density function • a 2D PDF is a surface, 3D PDF is a volume continuous X discrete X

42. Choose interpretation of highest probability • set X to be a skin pixel if and only if Where do we get and ? Probabilistic skin classification • Now we can model uncertainty • Each pixel has a probability of being skin or not skin • Skin classifier • Given X = (R,G,B): how to determine if it is skin or not?

43. Approach: fit parametric PDF functions • common choice is rotated Gaussian • center • covariance • orientation, size defined by eigenvecs, eigenvals Learning conditional PDF’s • We can calculate P(R | skin) from a set of training images • It is simply a histogram over the pixels in the training images • each bin Ri contains the proportion of skin pixels with color Ri This doesn’t work as well in higher-dimensional spaces. Why not?

44. Learning conditional PDF’s • We can calculate P(R | skin) from a set of training images • It is simply a histogram over the pixels in the training images • each bin Ri contains the proportion of skin pixels with color Ri • But this isn’t quite what we want • Why not? How to determine if a pixel is skin? • We want P(skin | R) not P(R | skin) • How can we get it?

45. what we measure (likelihood) domain knowledge (prior) what we want (posterior) normalization term Bayes rule • In terms of our problem: • The prior: P(skin) • Could use domain knowledge • P(skin) may be larger if we know the image contains a person • for a portrait, P(skin) may be higher for pixels in the center • Could learn the prior from the training set. How? • P(skin) may be proportion of skin pixels in training set

46. in this case , • maximizing the posterior is equivalent to maximizing the likelihood • if and only if • this is called Maximum Likelihood (ML) estimation Bayesian estimation • Bayesian estimation • Goal is to choose the label (skin or ~skin) that maximizes the posterior • this is called Maximum A Posteriori (MAP) estimation likelihood posterior (unnormalized) = minimize probability of misclassification • Suppose the prior is uniform: P(skin) = P(~skin) = 0.5

47. Skin detection results

48. H. Schneiderman and T.Kanade General classification • This same procedure applies in more general circumstances • More than two classes • More than one dimension • Example: face detection • Here, X is an image region • dimension = # pixels • each face can be thoughtof as a point in a highdimensional space H. Schneiderman, T. Kanade. "A Statistical Method for 3D Object Detection Applied to Faces and Cars". IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000) http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/hws/www/CVPR00.pdf