Local Stereo Matching Using Adaptive Local Segmentation

1. Local Stereo Matching Using Adaptive Local Segmentation Sanja Damjanovi´c, Ferdinand van der Heijden, and Luuk J. Spreeuwers International Scholarly Research Network (ISRN), May 2012

2. Outline • Introduction (Related Work) • Proposed Algorithm • Experimental Results • Conclusion

3. Introduction

4. Introduction • Window-based matching produces an incorrect disparity map: • e.g. the discontinuities are smoothed • Related Works… [21] [21]K.-J. Yoon and I.-S. Kweon. Adaptive support-weight approach for correspondence search. PAMI, 2006.

5. Objective • Propose a local stereo matching framework: • Based on an adaptive local segmentation • robust against local radiometricaldifferences • successfully recovers disparities: • around the objects edges • of thin objects • of the occluded region

6. ProposedAlgorithm

7. Framework

8. Preprocessing • Goal: make the input image more suitable for adaptive local segmentation • Problems: • Noise : low-textured region (uniform region) • Sampling errors : high-textured region • Apply a nonlinear intensity transformation

9. Preprocessing • Transformation: based on the interpolated sub-pixel samples by bicubic transform in the 4 neighborhoods

10. Preprocessing Before Before - Detail After - Detail

11. Adaptive Local Segmentation • Goal: prevent that the matching region contains the pixels with significantly different disparities • Ideas: • Uniform areas : low threshold • Textured areas : high threshold • Using local intensity variation measure • determine the level of local texture

12. Adaptive Local Segmentation • local intensity variation : • Horizontal central difference: • Vertical central difference: • Intensity variation measure: • I(x, y − 1/2) and I(x, y + 1/2) : vertical half-pixel shifts of image I

13. Adaptive Local Segmentation (low) red→ orange→ green→ blue (high) Local intensity variation levels

14. Adaptive Local Segmentation • Dynamic threshold(Td) for each range by a look-up table: ‧ T : constant • If | center pixel(x,y) – neighbor pixel | < Td(x,y) • same segment (support region)

15. W Adaptive Local Segmentation W x W reference window W : adjacent pixel(gray value) : central pixel(gray value) : threshold B (binary map)

16. Stereo Correspondence (Cost/Aggregation) • (1) BlBr→ B • zl / zr: pixels from the left/right matching window (within B) • (2) Subtract the central pixel values cl and cr from vectors zl and zr

17. Stereo Correspondence (Cost/Aggregation) • (3) Eliminate the outliers • Sum of squared differences(SSD): Np: the length of vectors zland zrfor disparity d. Support region vector → zland zr

18. Hybrid Winner-take-all • Goal: consider only disparities supported by a sufficient number • Result of hybrid WTA: number of pixels disparity range cost threshold : a set containing the number of pixels participating in the cost aggregation step : threshold(, )

19. Postprocessing • Goal:detect the disparity errors and correct them • Outliers: • Errors caused by low-textured areas larger than the initial window • Occlusion • Method: • Median filter • Histogram voting • Consistency check

20. Postprocessing • Histogram voting: • propagates disparities to the regions with close intensities Threshold:

21. Postprocessing repeated iteratively until there are no more updates to disparities in the map • Histogram voting • Counting the disparities along 8 radial directions: • Normalization: • New value:

22. Pre-processing &Post-processing none post-processing pre-processing post-processing + pre-processing

23. ExperimentalResults

24. Experimental Results • Parameters: Rank:49

25. Experimental Results Left Image Proposed Error Map Ground Truth

26. Conclusion

27. Conclusion • Introduce a approach for stereo matching: • Based on the adaptive local segmentation • Pre-processing : • smootheslow-textured areas • Sharpens texture edges • Post-processing : • Detect and recovers occluded and unreliable disparities