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Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence
Author(s) -
Zhe Liu,
Renaud Marlet
Publication year - 2012
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.26.16
Subject(s) - ransac , preprocessor , artificial intelligence , outlier , robustness (evolution) , computer science , graph , pattern recognition (psychology) , computer vision , matching (statistics) , feature extraction , feature (linguistics) , feature matching , mathematics , image (mathematics) , theoretical computer science , statistics , linguistics , philosophy , biochemistry , chemistry , gene
Finding reliable correspondences between sets of feature points in two images remains challenging in case of ambiguities or strong transformations. In this paper, we define a photometric descriptor for virtual lines that join neighbouring feature points. We show that it can be used in the second-order term of existing graph matchers to significantly improve their accuracy. We also define a semi-local matching method based on this descriptor. We show that it is robust to strong transformations and more accurate than existing graph matchers for scenes with significant occlusions, including for very low inlier rates. Used as a preprocessor to filter outliers from match candidates, it significantly improves the robustness of RANSAC and reduces camera calibration errors.

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