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

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Presentation on theme: "Learning to Perceive Coherent Objects Nimrod Dorfman, Daniel Harari, Shimon Ullman Weizmann Institute of Science WEIZMANN INSTITUTE OF SCIENCE COGSCI 2013."— Presentation transcript:

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

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

3 Object segregation is learned 3 Adults

4 4 How do we learn to segregate objects?

5 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 6

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

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

9 Intensity edges? 9 Boundary

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

11 Familiar object 11 Global

12 12 How does it actually work?

13 Moving object 13 Motion

14 Moving object 14 Figure Ground Unknown Motion

15 15 Motion BoundaryGlobal

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

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

18 Entire image 18 Boundary FigureBackground

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

20 Detection 20 Global

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

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

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 23

24 24


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