1. 1 Ecological Statistics and Perceptual Organization Charless Fowlkes work with David Martin and Jitendra Malik at University of California at Berkeley

2. 2 “ I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of color. Do I have 327? No. I have sky, house, and trees.”

3. 3 010011010....

4. 4 “ I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of color. Do I have 327? No. I have sky, house, and trees.” Laws of Organization in Perceptual Forms Max Wertheimer (1923)

5. 5 Perceptual Organization Grouping Figure/Ground

6. 6

7. 7 Grouping by proximity

8. 8 Grouping by similarity

9. 9 Grouping by similarity (of shape)

10. 10 Size and Surroundedness

11. 11 turnyourhead.com Familiarity / Meaningfulness

12. 12 Convexity

13. 13 Perceptual organization as a computational theory of vision?

14. 14 How do these cues apply to real world images? How are different cues combined? Why does the visual system use these cues?

15. 15 Ecological Validity Brunswik & Kamiya 1953: Gestalt rules reflect the structure of the natural world Attempted to validate the grouping rule of proximity of similars Brunswik was ahead of his time… we now have the tools. Egon Brunswik (1903-1955)

16. 16 Strategy 1.Collect high-level ground-truth annotations for a large collection of images 2.Develop computational models of cues for perceptual organization calibrated to ground-truth training data 3.Measure cue statistics and evaluate the relative “power” of different cues

17. 17

18. 18 30 subjects, age 19-23 1,458 person hours over 8 months 1,020 Corel images 11,595 Segmentations –color, gray, inverted/negated “You will be presented a photographic image. Divide the image into some number of segments, where the segments represent “things” or “parts of things” in the scene. The number of segments is up to you, as it depends on the image. Something between 2 and 30 is likely to be appropriate. It is important that all of the segments have approximately equal importance.”

19. 19 Berkeley Segmentation DataSet [BSDS]

20. 20 Scene Background Sky TreesShore Water Small Top LR Mermaid Foreground Rocks Base Land (a) (b) (c) Scene Background TreesShore Water Small Top LR Mermaid Foreground Rocks Base Land Scene Background TreesShore Water Small Top LR Mermaid Foreground Rocks Base Land Sky

21. 21 Overview Grouping –Local Boundary Detection –Local Human Performance Figure/Ground –Local Figure/Ground Cues –Local Human Performance Discussion

22. 22 Non-BoundariesBoundaries T BC

23. 23 Gradient Features Brightness Gradient (BG) –Difference of brightness distributions Color Gradient (CG) –Difference of color distributions Texture Gradient (TG) –Difference of distributions of V1-like filter responses 1976 CIE L*a*b* color space Distributions are represented by smoothed histograms  r (x,y)

24. 24 Local Boundary Detection Image Boundary Cues Model PbPb Brightness Color Texture Using training data to learn the posterior probability of a boundary P(b=1|x,y,  ) from local gradient information Logistic regression to combine cues Cue Combination Brightness Color Texture

25. 25 Canny Pb HumanImage

26. 26 Canny Pb Humans Image

27. 27

28. 28 Goal Fewer False Positives Fewer Misses

29. 29 Recall = P(P b > t | H = 1) Precision P(H = 1 | P b > t)

30. 30 How good are humans locally? Off-Boundary On-Boundary Algorithm: r = 9, Humans: r = {5,9,18} Fixation(2s) -> Patch(200ms) -> Mask(1s)

31. 31 Man versus Machine:

32. 32 Findings Texture gradient information is important for natural scenes Optimal local cue combination is achievable with a simple linear model Algorithm for performing local boundary detection which performs nearly as well as local humans (and better than traditional edge detectors).

33. 33 Overview Grouping –Local Boundary Detection –Local Human Performance Figure/Ground –Local Figure/Ground Cues –Local Human Performance Discussion

34. 34 Local Cues for Figure/Ground Assume we have a perfect segmentation Can we predict which region a contour belongs to based on its local shape? –Size –Convexity –Lower Region

35. 35 Figure-Ground Labeling - start with 200 segmented images of natural scenes - boundaries labeled by at least 2 different human subjects - subjects agree on 88% of contours labeled

36. 36 Size(p) = log(Area F / Area G ) Size and Surroundedness [Rubin 1921] G F p

37. 37 Convexity(p) = log(Conv F / Conv G ) Conv G = percentage of straight lines that lie completely within region G p G F Convexity [Metzger 1953, Kanizsa and Gerbino 1976]

38. 38 LowerRegion(p) = θ G Lower Region [Vecera, Vogel & Woodman 2002] θ center of mass

39. 39 Size Lower Region Convexity

40. 40 Figural regions tend to lie below ground regions

41. 41 Figural regions tend to be convex

42. 42 Figural regions tend to be small

43. 43 “Upper Bounding” Local Performance Present human subjects with local shapes, seen through an aperture. ConfigurationConfiguration + Content

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46. 46

47. 47 Findings Convexity, size and lower-region are ecologically valid. Boundary configuration is relatively weak compared to luminance content. Local judgments based on luminance content can be quite accurate.

48. 48 How do these cues apply to real world images? How are different cues combined? Why does the visual system use these cues? Perceptual organization as a computational theory of vision

49. 49 How do ideas from perceptual organization relate to natural scenes?

50. 50 How do ideas from perceptual organization relate to natural scenes?

51. 51 THE END