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Scale Invariant Feature Transform (SIFT) is a very powerful technique for image registration. While SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approach for registration is overly simplified, because it completely ignores the geometric information among descriptors. In this paper, we formulate keypoint matching as a global optimization...
Stereo matching has been one of the most active research areas in computer vision for decades. Many methods, ranging from similarity measures to local or global matching cost optimization algorithms, have been proposed. As we known, stereo matching can be formulated under the framework of Markov random field (MRF), and the global optimization in stereo matching can be approximated by inference procedure...
In this paper, feature point matching is formulated as an optimization problem in which the uniqueness condition is constrained. We propose a novel score function based on homography-induced pairwise constraints, and a novel optimization algorithm based on relaxation labeling. Homography-induced pairwise constraints are effective for image pairs with viewpoint or scale changes, unlike previous pairwise...
Partial matching is probably one of the most challenging problems in nonrigid shape analysis. The problem consists of matching similar parts of shapes that are dissimilar on the whole and can assume different forms by undergoing nonrigid deformations. Conceptually, two shapes can be considered partially matching if they have significant similar parts, with the simplest definition of significance being...
In this paper, a stereo correspondence algorithm based on ground control points (GCPs) is presented. First of all, a new scheme for determining GCPs is introduced, where the Hausorff distance is improved to find out the most possible and robust matches. With additional space information provided by the Hausdorff distance, the algorithm effectively eliminates outliers and mismatches of control points...
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