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ISSUES IN 3D OBJECT RECOGNITION Jean Ponce Department of Computer Science and Beckman Institute University of Illinois at Urbana-Champaign Joint work with.

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Presentation on theme: "ISSUES IN 3D OBJECT RECOGNITION Jean Ponce Department of Computer Science and Beckman Institute University of Illinois at Urbana-Champaign Joint work with."— Presentation transcript:

1 ISSUES IN 3D OBJECT RECOGNITION Jean Ponce Department of Computer Science and Beckman Institute University of Illinois at Urbana-Champaign Joint work with Amit Sethi, David Renaudie and David Kriegman and Svetlana Lazebnik, Cordelia Schmid and Martial Hebert

2 Face Camel Bug Human/Felix Joe Barbara Steele Problem: Recognizing instances Recognizing categories

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4 Variability : Camera position Illumination Internal parameters Within-class variations

5 Question #1: Is it better to eliminate as many possible of the parameters that govern appearance or is it better to work with the raw pixels? Note: We may know something about the “shape” and the “dimension” of our image set. This “surface” is not smooth.

6 Brooks and Binford, 1981 Sullivan and Ponce, 1998 Murase and Nayar, 1992 Schmid and Mohr, 1996 Invariants (Weiss, 1988; Rothwell et al., 1992; etc.)

7 Face Camel Bug Human ??

8 Question #2: What is an appropriate object representation for describing people, animals, chairs, boats, shoes, etc. ?? Do we really believe that local pixel signatures and their geometric/statistical relationships are sufficient? or

9 The Blum transform, 1967 Generalized cylinders Binford, 1971

10 Zhu and Yuille, 1996

11 Question #3: How do we construct object descriptions from images? = How do we segment images? = How do we compute our feature vectors?

12 Forsyth, 2000

13 Question #4: How can we formalize the object recognition process? What should the corresponding optimization process try to optimize? or

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16 The dual

17 d1d2 d3 d1 d2 d3 The pedal curve The trace

18 d1 d2 d3 d 

19 d3 Occluding contour Silhouette

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24 Dim.

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26 Question #5: How can we effectively deal with clutter ?

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29 Baseline Frontier point 3D/4D  1  3 (Cipolla, Åström and Giblin, 1995)

30 How do we recognize objects at the category level? Question #6:


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