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Learning to Perceive Coherent Objects

WEIZMANN INSTITUTE OF SCIENCE. COGSCI 2013. Learning to Perceive Coherent Objects. Nimrod Dorfman , Daniel Harari , Shimon Ullman Weizmann Institute of Science. Object segregation is learned. Even basic Gestalt cues are initially missing [Schmidt et al. 1986 ]. 5 months.

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Learning to Perceive Coherent Objects

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  1. WEIZMANN INSTITUTE OF SCIENCE COGSCI 2013 Learning to PerceiveCoherent Objects Nimrod Dorfman, Daniel Harari, Shimon Ullman Weizmann Institute of Science

  2. Object segregation is learned Even basic Gestalt cues are initially missing [Schmidt et al. 1986] 5 months

  3. Object segregation is learned Adults

  4. How do we learnto segregate objects?

  5. We propose a computational model: • Explain the first steps of learning • Based on psychophysical findings • Computationally tested on videos

  6. It all begins with motion Motion

  7. It all begins with motion Grouping by common motion precedes figural goodness [Spelke 1990 - review] Motion discontinuities provide an early cue for occlusion boundaries [Granrud et al. 1984]

  8. Our model Motion-based segregation Boundary General Accurate Noisy Incomplete Motion discontinuities Common motion Global Object-specific Complete Inaccurate Local occlusion boundaries Object form Static segregation

  9. Intensity edges? Boundary

  10. Occlusion cues Boundary T-junctions Convexity Extremal edges [Ghose & Palmer 2010]

  11. Familiar object Global

  12. How does it actually work?

  13. Moving object Motion

  14. Moving object Motion Figure Ground Unknown

  15. Motion Boundary Global

  16. Good examples Boundary Need many examples for good results (1000+)

  17. Prediction Boundary Figure or Ground? Figure or Ground? Novel object, novel background 78% success Using 100,000 training examples

  18. Entire image Boundary Figure Background

  19. Learning an object Global Standard object recognition algorithm Learns local features and their relative locations

  20. Detection Global

  21. Combining information sources Boundary Accurate Noisy & Incomplete Global Complete Inaccurate Combined

  22. More complex algorithms Default GrabCut With segregation cue [Rother et al. 2004]

  23. Summary • Static segregation is learned from motion • Two simple mechanisms: Boundary Motion discontinuities  Occlusion boundaries (Need a rich library, including extremal edges) Global Common motion  Object form • These mechanisms work in synergy • This is enough to get started, adult segregation is much more complex

  24. Thank You!

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