TY - GEN
T1 - A local iterative refinement method for adaptive support-weight stereo matching
AU - Psota, Eric T.
AU - Kowalczuk, Jȩdrzej
AU - Carlson, Jay
AU - Perez, Lance C.
PY - 2011
Y1 - 2011
N2 - A new stereo matching algorithm is introduced that performs iterative refinement on the results of adaptive support-weight stereo matching. During each iteration of disparity refinement, adaptive support-weights are used by the algorithm to penalize disparity differences within local windows. Analytical results show that the addition of iterative refinement to adaptive support-weight stereo matching does not significantly increase complexity. In addition, this new algorithm does not rely on image segmentation or plane fitting, which are used by the majority of the most accurate stereo matching algorithms. As a result, this algorithm has lower complexity, is more suitable for parallel implementation, and does not force locally planar surfaces within the scene. When compared to other algorithms that do not rely on image segmentation or plane fitting, results show that the new stereo matching algorithm is one of the most accurate listed on the Middlebury performance benchmark.
AB - A new stereo matching algorithm is introduced that performs iterative refinement on the results of adaptive support-weight stereo matching. During each iteration of disparity refinement, adaptive support-weights are used by the algorithm to penalize disparity differences within local windows. Analytical results show that the addition of iterative refinement to adaptive support-weight stereo matching does not significantly increase complexity. In addition, this new algorithm does not rely on image segmentation or plane fitting, which are used by the majority of the most accurate stereo matching algorithms. As a result, this algorithm has lower complexity, is more suitable for parallel implementation, and does not force locally planar surfaces within the scene. When compared to other algorithms that do not rely on image segmentation or plane fitting, results show that the new stereo matching algorithm is one of the most accurate listed on the Middlebury performance benchmark.
KW - Adaptive support weights
KW - Iterative stereo
KW - Stereo correspondence
KW - Stereo matching
UR - http://www.scopus.com/inward/record.url?scp=84864924184&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864924184&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84864924184
SN - 9781601321916
T3 - Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
SP - 271
EP - 277
BT - Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
T2 - 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
Y2 - 18 July 2011 through 21 July 2011
ER -