Segment-based stereo matching using graph cuts

By Li Hong, George Chen. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004

363 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Symmetric stereo matching for occlusion handling (2005, background): “, [20, 10, 2, 14, 22]) have demonstrated that the difficulties and ambiguities caused by textureless or occlusion can be handled by using groups of pixels with similar colors.”
Continuous Markov Random Fields for Robust Stereo Estimation (2012, methodology): “Slatend-plane MRF models for stereo vision were introduced in [4] and have been since very widely used [5–7, 9, 10, 13].”
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure (2006, methodology): “Global approaches are incorporating explicit smoothness assumptions and are determining all disparities simultaneously by applying energy minimization techniques such as graph cuts [2, 4, 6, 7], belief propagation [5, 9, 10, 12], dynamic programming, scanline optimization or simulated annealing.”
Review of Stereo Vision Algorithms: From Software to Hardware (2008, methodology): “Thestereomatching algorithm proposed in (Hong and Chen 2004) makes use of color segmentation in conjunction with the graph cuts method.”