Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a self-adapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique...
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| creator | Klaus, A. Sormann, M. Karner, K. |
| description | A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a self-adapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique that is more robust to outliers. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. The optimal disparity plane labeling is approximated by applying belief propagation. Experimental results using the Middlebury stereo test bed demonstrate the superior performance of the proposed method |
| doi_str_mv | 10.1109/ICPR.2006.1033 |
| format | Conference Proceeding |
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| ispartof | 18th International Conference on Pattern Recognition (ICPR'06), 2006, Vol.3, p.15-18 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_1699458 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Belief propagation Brightness Image segmentation Image sensors Labeling Layout Pixel Robustness Signal to noise ratio Testing |
| title | Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure |
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