Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Geo-Consistency for Wide Multi-Camera Stereo
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Asymmetrical Occlusion Handling Using Graph Cut for Multi-View Stereo
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
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In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) are merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. Our method solves the problem on the base of a real matching cost, in such a way a global optimization problem is solved just once, and the resultant solution does not have to be corrected in the occluded regions. In contrast, the traditional methods that use two images approach have to complicate a lot their algorithms by additional add hog or heuristic techniques to reach competitive results in occluded regions.