A global matching framework for stereo computation

By Hai Tao, Harpreet S. Sawhney, Rakesh Kumar. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001

308 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/61a1022330f3d7f8fafef38efe8771d21731187d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Manhattan-world stereo (2009, background): “[19], have specialized the 2D affine motion models first suggested by Wang and Adelson to the rigid multi-view stereo setting.”
High-quality video view interpolation using a layered representation (2004, background): “Tao et al. [2001] assume the scene is piecewise planar.”
High performance imaging using large camera arrays (2005, methodology): “2004] and analysis of structure and motion in dynamic scenes [Tao et al. 2001; Zhang and Kambhamettu 2001].”
Guiding model search using segmentation (2005, methodology): “[18] who used colour segmentation as pre-processing for stereo matching.”
Recovering Surface Layout from an Image (2007, background): “Superpixels correspond to small, nearly-uniform regions in the image and have been found useful by other computer vision and graphics researchers (Tao et al., 2001; Ren and Malik, 2003; Li et al., 2004).”
Associative hierarchical CRFs for object class image segmentation (2009, unknown): “The last few years have seen a proliferation of unsupervised segmentation methods [5, 8, 24], that perform an initial a priori segmentation of the image, applied to object segmentation [1, 10, 32, 11, 22, 32], and elsewhere [13, 27].”
Stereo for Image-Based Rendering using Image Over-Segmentation (2007, methodology): “In (Tao et al., 2001), neighboring segments are used to help in finding correct correspondences in the vicinity of occlusions.”
Segment-based stereo matching using graph cuts (2004, methodology): “[10] and the graph cuts-based algorithms of Boykov et al.”
PatchMatch Stereo - Stereo Matching with Slanted Support Windows (2011, methodology): “, [2, 12]) extract several planes using an initial disparity map in the first step.”
Stereo Matching Using Belief Propagation (2002, methodology): “Recently, a segmentation-based stereo algorithm [32] has been proposed based on the assumption that the depth discontinuities occur on the boundary of the segmented regions.”
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure (2006, methodology): “Recently, segment-based methods [1, 2, 4, 6, 11] have attracted attention due to their good performance.”
Automatic photo pop-up (2005, methodology): “Superpixels correspond to small, nearly-uniform regions in the image and have been found useful by other computer vision and graphics researchers [Tao et al. 2001; Ren and Malik 2003; Li et al. 2004].”
Learning Conditional Random Fields for Stereo (2007, background): “Taken to an extreme, this translates into the assumption that disparity jumps always coincide with color edges, which is the basis of a large number of recent segmentation-based stereo methods [10, 14–19].”
Symmetric stereo matching for occlusion handling (2005, methodology): “, [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.”
Adaptive Support-Weight Approach for Correspondence Search (2006, unknown): “Index Terms—Stereo, 3D/stereo scene analysis.”
Consistent Depth Maps Recovery from a Video Sequence (2009, methodology): “To obtain a 3D surface representation of an object, Hernández et al. [18] proposed a probabilistic framework to model geometric occlusion in a probabilistic way.”