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Boundary-preserving Stereo Matching based on Confidence Region Detection and Disparity Map Refinement
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 Title & Authors
Boundary-preserving Stereo Matching based on Confidence Region Detection and Disparity Map Refinement
Yun, In Yong; Kim, Joong Kyu;
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In this paper, we propose boundary-preserving stereo matching method based on adaptive disparity adjustment using confidence region detection. To find the initial disparity map, we compute data cost using the color space (CIE Lab) combined with the gradient space and apply double cost aggregation. We perform left/right consistency checking to sort out the mismatched region. This consistency check typically fails for occluded and mismatched pixels. We mark a pixel in the left disparity map as "inconsistent", if the disparity value of its counterpart pixel differs by a value larger than one pixel. In order to distinguish errors caused by the disparity discontinuity, we first detect the confidence map using the Mean-shift segmentation in the initial disparity map. Using this confidence map, we then adjust the disparity map to reduce the errors in initial disparity map. Experimental results demonstrate that the proposed method produces higher quality disparity maps by successfully preserving disparity discontinuities compared to existing methods.
Confidence map;Mean-shift segmentation;Adaptive hole filling;Adaptive weighted filter;
 Cited by
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