Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding the Largest Unambiguous Component of Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stable Monotonic Matching for Stereoscopic Vision
RobVis '01 Proceedings of the International Workshop on Robot Vision
A Binocular Stereo Algorithm for Log-Polar Foveated Systems
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Neural adaptive stereo matching
Pattern Recognition Letters
Neural disparity computation for dense two-frame stereo correspondence
Pattern Recognition Letters
Coarse-to-fine stereo vision with accurate 3D boundaries
Image and Vision Computing
View synthesis using stereo vision
View synthesis using stereo vision
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We present a method for computing dense visual correspondence based on general assumptions about scene geometry. Our algorithm does not rely on correlation, and uses a variable region of support. We assume that images consist of a number of connected sets of pixels with the same disparity, which we call {\em disparity components}. At each pixel we compute a small set of plausible disparities, each of which is more likely to be the pixel's true disparity than not. A pixel is assigned a disparity $d$ based on connected components of pixels, where each pixel in a component considers $d$ to be plausible. Our implementation chooses the largest plausible disparity component; however, global contextual constraints can also be applied. While the algorithm was originally designed for visual correspondence, it can also be used for other early vision problems such as image restoration. It runs in a few seconds on traditional benchmark images with standard parameter settings, and gives quite promising results.