Edge and Boundary interpretation - PowerPoint PPT Presentation

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Edge and Boundary interpretation

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Edge and Boundary interpretation Consistent line drawing labeling via backtracking Presented by Guy Shtub Perceiving 3D from 2D How can humans and machines ... – PowerPoint PPT presentation

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Title: Edge and Boundary interpretation


1
Edge and Boundary interpretation
  • Consistent line drawing labeling via backtracking
  • Presented by Guy Shtub

2
Perceiving 3D from 2D
  • How can humans and machines reconstruct the 3D
    nature of a scene from a 2D image representing
    them?
  • Additional knowledge is needed

3
Assumptions
  • Trihedral (formed by 3 planes meeting at a point)
    world.
  • General viewpoint
  • Edges represent surface and depth continuities

4
Huffman and Clowes catalog 1971
  • Define edge type symbols

5
  • Define 16 possible edge intersections

6
Constraint Satisfaction Problems (CSPs)
  • Consist of a set of variables
  • Each variable must be assigned a value from a
    possible domain
  • Backtracking attempts to find a solution be
    trying all combinations in order to find a
    solution
  • Line labeling is a CSP

7
Naïve Backtracking 4 queens Example
8
Forward Checking (FC) - 4 Queens Example
9
Problem Representation
  • Edge intersections are variables - N
  • 16 catalog possibilities are the domain for each
    variable - D
  • Variable Conflicts Matrix NxN (adjacent
    intersections)
  • For each pair of adjacent intersections a
    Constraints matrix DxD
  • Both Algorithms implemented in Java

10
Algorithm Performance Evaluation
  • Constraint Checks (CCs) machine and
    implementation independent

11
Results
12
Results performance
13
Conclusions
  • Written Program able to provide consistent line
    labeling
  • As expected FC performs better the Naïve
    Backtracking
  • Future work comparison to line labeling via
    relaxation labeling performance
  • Questions?

14
References
  • M. B. Clowes, "On seeing things," ltigtArtificial
    Intelligencelt/igt, Vol. 2, No. 1, pp. 79-116,
    1971.
  • D.A. Huffman, Impossible objects as nonsense
    sentences, Machine Imlligencc 6, pp,
    295-323,1971.
  • Depth and Shape Inference (II), Introduction to
    Computational and Biological Vision, Computer
    Science Department, BGU, Ohad Ben-Shahar
  •  P. Prosser "Hybrid algorithms for the
    constraint satisfaction problem", Computational
    Intelligence, Vol. 9, pp. 268-99, 1993.
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