Download
stereo vision using patchmatch algorithm n.
Skip this Video
Loading SlideShow in 5 Seconds..
Stereo Vision using PatchMatch Algorithm PowerPoint Presentation
Download Presentation
Stereo Vision using PatchMatch Algorithm

Stereo Vision using PatchMatch Algorithm

828 Vues Download Presentation
Télécharger la présentation

Stereo Vision using PatchMatch Algorithm

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

  2. 1. Reference

  3. Reference • Primary • PatchMatch : A Randomized Correspondence Algorithm (Barnes et al, PAR 2009) • Secondary • A Computational Theory of Human Stereo Vision (Marr & Poggio, 1979) • PatchMatch Stereo - Stereo Matching with Slanted Support Windows (Bleyer, Rhemann, and Rother, BMVC 2011) • Dataset • Middlebury Dataset (later)

  4. 2. Summary of Work

  5. Summary of Work • Implemented PatchMatch stereo in MATLAB • Implemented additional features • Ability to constrain search space by disparity • Ability to use weighted distance measure • Min-Distance minimization* • Performed comprehensive evaluation

  6. 3. Evaluation

  7. Evaluation • How evaluation is done • Dataset • Middlebury 2006 dataset • Hiebert-Treueret al, CVPR 2007 • 21 binocular stereo images • Used sixth-size images • half of third-size images provided : 413~465 x 370

  8. Evaluation • How evaluation is done • Parameter-wise evaluation • Focused on the performance (accuracy) gain / loss across different parameter dimensions • To account for rate of convergence, took 3-iteration output for every configuration.

  9. Evaluation • How evaluation is done • Measure of error • Squared error of output disparity map from ground-truth disparity map. • Sum(sum((GroundTruth – Output).^2)) • Took average error across 21 images, along with +,- two standard deviations

  10. Evaluation • 1. Propagation stage • Built-in PatchMatchfeature • Takes advantage of smoothness constraint • Does not enforce it

  11. Evaluation • 2. Constrained search space • Avoids possible false local minima • Maybe useful for fixed-camera applications

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

  13. Evaluation • 3. Patch size • Built-in PatchMatch feature • Smaller Patch size • Less smoothness enforced • Smaller sample space • Larger Patch size • Generally leads to more accuracy • Does not work for non-frontoparallel space • Does not work for locations of depth discontinuity

  14. PatchMatch on Stereo

  15. Evaluation • 3. Representation : RGB • Baseline : Grayscale • Increase selectivity

  16. PatchMatch on Stereo

  17. PatchMatch on Stereo

  18. Evaluation • 3. Representation : Filtered output • Filter : Derivative of Gaussians • “Edge detectors” • One scale (11), two phases, 6 orientations • Tradeoff : heavier computational load • times O(# of filters)

  19. PatchMatch on Stereo

  20. PatchMatch on Stereo

  21. PatchMatch on Stereo

  22. PatchMatch on Stereo

  23. Evaluation • 3. Representation : Filtered output • Improved performance in certain range of patch sizes • Trying multiscale oriented DoG’s will probably improve performance further • Might increase error at locations with high orientation disparity

  24. Evaluation • 3. Representation : Filtered output • Orientation disparity is mostly limited to highly horizontally slanted surfaces • Even in those cases, the effect is not very significant, given small ratio of binocular distance to the distance from the camera to the surface

  25. Evaluation • 3. Representation : Filtered output • Orientation disparity is mostly limited to highly horizontally slanted surfaces • Even in those cases, the effect is not very significant, given small ratio of binocular distance to the distance from the camera to the surface

  26. Evaluation • 4. Support : Gaussian Mask • Baseline : uniform, square window • ‘loosens’ the assumption on frontoparallel surface by differentially decreasing weight as you move away from the center.

  27. PatchMatch on Stereo

  28. PatchMatch on Stereo

  29. Evaluation • Limitations • Every surface might require different support. • Gaussian should be elongated orthogonal to the direction of slant. • Problem is, we need a way for the system to figure out which. • Symmetric masks can’t capture the correct surface constraints at surface discontinuity • Can be ideally solved by graph-cuts, but impossible under PatchMatch framework.

  30. Evaluation • 4. Support : Min-minimization • Slightly different approach to masking method. • Instead of one, compute distance using multiple masks of different shapes (normalized), each reflecting different surface configuration in support window • Pick the minimum distance (presumably the one computed using the ‘correct’ surface support) • I used half-rectified Gabors • To avoid bias, I applied CS gaussian over each mask • Again, the problem is multiplied computational time

  31. PatchMatch on Stereo

  32. PatchMatch on Stereo

  33. PatchMatch on Stereo

  34. Evaluation • Reduced error the most • Small increase in variance • High increase in computation time

  35. End