1. Measuring the Ecological Statistics of Figure-Ground Charless Fowlkes, David Martin, Jitendra Malik

2. Is there an Ecological Justification for Figure-Ground Cues? Size Surroundedness Convexity Lower-Region Symmetry … Are figural regions in the natural world really more convex?

3. Figure-Ground Labeling 200 images each labeled by 2 subjects

4. Consistency – 88% agreement Agreement doesn’t differ with edge length

5. Local Figural Assignment Cues Size and Surroundedness [Rubin 1915] Convexity [Metzger,Kanizsa] Lower-Region [Vecera, Vogel & Woodman 2002]

6. Size(p) = log(A F / A G ) Size : G F p

7. Convexity(p) = log(C F / C G ) Convexity:

9. Aboveness(p) = cos(  ) Aboveness:  center of mass

10. Empirical Frequencies of Size, Convexity and Aboveness. 1200 sample points per image

12. Local Boundary Detection in Natural Images: Matching Human and Machine Performance Dave Martin, Charless Fowlkes, Laura Walker, Jitendra Malik

13. Boundary Detection Image Boundary Cues Model PbPb Challenges: texture cue, cue combination Goal: learn the posterior probability of a boundary P b (x,y,  ) from local information only Cue Combination Brightness Color Texture

14. Non-BoundariesBoundaries T BC

15. Two Decades of Boundary Detection

16. Local Boundary Detection Solved? Clearly top-down, high level knowledge is utilized by humans

17. Test Humans on Local Patches

19. Did you see a boundary running through the center of the patch? [Y/N]

20. radius: 9, 18, 36 humans: 78, 83, 85 F-Measure at r = 9 Humans: 78 Machines: 78