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Local Stereo Matching Using Adaptive Local Segmentation

This paper presents a robust local stereo matching framework utilizing adaptive local segmentation. The proposed method addresses common issues in disparity map generation, particularly around object edges and occluded regions, by adapting to local radiometrical differences. The framework includes preprocessing that enhances image suitability and adaptive segmentation that differentiates matching regions based on local texture variation. The effectiveness is evaluated through experimental results, demonstrating improved accuracy in disparity recovery in challenging areas, highlighting potential applications in computer vision.

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Local Stereo Matching Using Adaptive Local Segmentation

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  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

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