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Stereo Vision using PatchMatch Algorithm

Stereo Vision using PatchMatch Algorithm. Junkyung Kim Class of 2014. 1. Updates. Updates. Source code written in C Re-implemented PatchMatch in MATLAB With some additional features (3). Updates. Additional Features 1. Ability to use weighted distance measures Uniform by default

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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. Updates

  3. Updates • Source code written in C • Re-implemented PatchMatch in MATLAB • With some additional features (3)

  4. Updates • Additional Features • 1. Ability to use weighted distance measures • Uniform by default • RBF, Gabor, Vertical Bar • (hopefully) Reduce ambiguity / being caught in local minimum

  5. Updates • Additional Features • 2. Ability to choose sampling distribution • Exponential distribution by default • U, G, chi-squared, etc • (hopefully) accelerate convergence to optimal NNF

  6. Updates • Additional Features • 3. Ability to further restrict search space • ‘Disparity range’ is smaller than the entire epipolar line. • (Surely) accelerate convergence & avoid false matches • User – defined parameter. Hybrid System.

  7. 2. PatchMatch on Stereo

  8. PatchMatch on Stereo • = Constrained correspondence search algorithm • Entire image -> Epipolar line • Epipolar line -> User-defined disparity band (additional feature)

  9. PatchMatch on Stereo • Pipeline • 1. Random Initialization • 2. Propagation • 3. Random Search • Repeat stages 2. and 3. for fixed number of iterations • Or until threshold error is reached

  10. PatchMatch on Stereo • 1. Random Initialization • Within the search space (epipolar line by default), • randomly assign offset between two images

  11. PatchMatch on Stereo • 1. Random Initialization • Within the search space (epipolar line by default) • randomly assign offset between two images

  12. PatchMatch on Stereo • 2. Propagation • Compare current offset and those of previous pixels • One pixel above & One pixel to the left

  13. PatchMatch on Stereo • 2. Propagation • Compare current offset and those of previous pixels • One pixel above & One pixel to the left

  14. PatchMatch on Stereo • 2. Propagation • Compare current offset and those of previous pixels • One pixel above & One pixel to the left

  15. PatchMatch on Stereo • 2. Propagation • Compare current offset and those of previous pixels • One pixel above & One pixel to the left

  16. PatchMatch on Stereo • 2. Propagation • Choose offset that minimizes distance measure

  17. PatchMatch on Stereo • 3. Random Search • Sample candidate offsets in exponential distribution, centered at the current offset

  18. PatchMatch on Stereo • 3. Random Search • Sample candidate offsets in exponential distribution, centered at the current offset

  19. PatchMatch on Stereo • 3. Random Search • Sample candidate offsets in exponential distribution, centered at the current offset

  20. PatchMatch on Stereo • 3. Random Search • Choose offset that minimizes distance measure

  21. PatchMatch on Stereo • The algorithm is bound to reach the global minimum • As defined by global minimum of distance measure (L2 – distance by default)

  22. PatchMatch on Stereo • Exhaustive search achieves the same solution • Why use random sampling instead of brute-force? • Expected to quickly converge the near-optimum in small number of iterations.

  23. PatchMatch on Stereo • How quickly does this algorithm approach global minimum?

  24. 3. Results

  25. PatchMatch on Stereo

  26. PatchMatch on Stereo

  27. PatchMatch on Stereo

  28. PatchMatch on Stereo

  29. PatchMatch on Stereo

  30. PatchMatch on Stereo

  31. PatchMatch on Stereo

  32. PatchMatch on Stereo

  33. PatchMatch on Stereo Black : Baseline / Green : Constrained disp, propagation off / Blue : Constrained disp, propagation on / Red : Exhaustive Search

  34. 4. Comments &Future Work

  35. Comments &Future Work • Propagation is certainly helping with speed • Smoothness constraint holds • Practical advantage in constraining disparity • Stereo cameras (bumblebee, etc.) usually have fixed vergence point (+inf). • No need to consider uncrossed disparity

  36. Comments &Future Work • PatchMatch achieves global optimum with enough iterations • Minimum of 5 with constraints • How good is the global optimum compared to ground truth? • There must be good amount of error

  37. Comments &Future Work • Orientation disparity • Half occlusion • Distortion • Other tricks can deal with those issues better

  38. Comments &Future Work • For project final • Try feature map for representation • Oriented edges / bars will handle orientation disparity better, more robust to distortion • Try different sampling distributions • Giving more weight at the center will increase accuracy along the physical contours

  39. PatchMatch on Stereo • Pipeline • 1. Random Initialization • 2. Propagation • 3. Random Search • Repeat stages 2. and 3. for fixed number of iterations • Or until threshold error is reached

  40. End

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