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Stereo Vision using PatchMatch Algorithm

Stereo Vision using PatchMatch Algorithm. Junkyung Kim Class of 2014. 1. Introduction. Vision Problem Revisited. Loss of information 3-D physical world projected onto a 2-D surface. Vision Problem Revisited. Therefore, an inherently Ill-posed problem. Vision Problem Revisited.

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Stereo Vision using PatchMatch Algorithm

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  1. Stereo Vision using PatchMatch Algorithm Junkyung Kim Class of 2014

  2. 1. Introduction

  3. Vision Problem Revisited • Loss of information • 3-D physical world projected onto a 2-D surface

  4. Vision Problem Revisited • Therefore, an inherently Ill-posed problem

  5. Vision Problem Revisited • Possible solutions • 1. Directly exploit the Well-behavednessof the physical world • Shading • Occlusion • Textural transformation & alignment …

  6. Vision Problem Revisited • Possible solutions • 2. Rely on different sources of information • Ecolocation • “Kinect” …

  7. Vision Problem Revisited • Possible solutions • 2. Use multiple 2-D images • Motion • Stereo

  8. 1. Description of the problem

  9. What is Stereo Vision? • A constrained version of motion parallax • When the observer moves, closer objects appear to move faster • Constraint 1 : only two frames (binocular) • Constraint 2 : observer moves only laterally • Constraint 3 : all the objects are stationary

  10. What is Stereo Vision? • Binocular disparity for depth computation

  11. What is Stereo Vision? • How to get depth? = How to get delta-X ? • Must find the correspondence pairing first www.consortium.ri.cmu.edu

  12. What is Stereo Vision? • How to get depth? = How to get delta-X ?

  13. Correspondence Problem • Ambiguity, again • Exhaustive search, even though reduced only within the epipolar line, is highly prone to false pairing • 1. inherent imbalance : search space >> sample space • 2. noise

  14. Correspondence Problem • Ambiguity, again • Worst-case scenario : binary RDS

  15. Correspondence Problem • Solutions • 1. Use more evidence (neighboring pixels) • Equivalent to increasing sample space • Better-posed than ill-posed • 2. Enforce well-behavedness of the world • smoothness

  16. Correspondence Problem • Implementation • 1. Representational • Some feature map rather than raw image • 2. Computational • PatchMatch

  17. 3. Method : PatchMatch

  18. PatchMatch • A Randomized Correspondence Algorithm for Structural Image Editing • Barnes, et al • Patten Analysis & Recognition, 2009

  19. PatchMatch • Proposed Applications • Image reconstruction (reshuffling) Barnes et al, 2009

  20. PatchMatch • Proposed Applications • Image completion (inpainting) Barnes et al, 2009

  21. PatchMatch • Proposed Applications • Image retargeting (transformations) Barnes et al, 2009

  22. PatchMatch • Why is it suitable for stereo correspondence? • 1. patch-based match • Nearest-Neighbor Field (NNF) • large sample space > uniqueness constraint better satisfied

  23. PatchMatch • Why is it suitable for stereo correspondence? • 2. computational efficiency : • randomized search followed by propagation • Not just faster, but easier to implement smoothness enforcement • Important in application (e.g. navigation)

  24. 4. Milestones

  25. Milestones • Week 1~2 • Study PatchMatchand source code • Gather dataset to work on • Preferrably with one with ground-truth disparity map

  26. Milestones • Week 3~5 (mid-project presentation) • Preliminary implementation • Generate first-round of outputs

  27. Milestones • Week 6~9 (final presentation) • Evaluate the algorithm with full outputs • (if time allows) Draw comparison among several different implementations

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