Computer Vision, Graphics, and Image Processing
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Artificial Intelligence - Special volume on computer vision
Computer Vision and Image Understanding
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Contour Matching Using Epipolar Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An algorithm for contour matching is presented in this paper. It is implemented in two steps: firstly, bottom-up, corners are matched, the matched corner points guide line segment matching, and then the matched line segments guide contour matching. Line segments are grouped with the signature function defined in [10] from the extracted contours ofimage pairs. Secondly, top-down, with the computed signature functions of the matched contours, the contour points are corresponded by modifying the initial matching from line segments.The novelty ofour approach is that (1) features are incorporated, and the matching is implemented in two steps (bottom-up and top-down), thus dense correspondences along contours are acquired. Disadvantages such as the sparseness from only point correspondences, the inaccuracies from only line correspondences, and feature loss of correspondences along epipolar lines are avoided; (2) unlike the conventional way ofmatc hing along epipolar lines after rectification, signature functions developed in [10] are used to characterize the contours so that the matching is implemented along contours.