Performance of optical flow techniques
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Stereo Correspondence with Occlusion Handling in a Symmetric Patch-Based Graph-Cuts Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces with occlusions from layered stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, then propagates to the neighboring pixels, finally the most points in the images are matched. During matching, we apply the convolution to the normalized cross correlation (NCC).The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images are less textured.In particular, with big images and a large disparity range our algorithm turns out to be significantly faster.