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<p>This correspondence presents a matching algorithm for obtaining feature point correspondences across images containing rigid objects undergoing different motions. First point features are detected using newly developed feature detectors. Then a variety of constraints are applied starting with simplest and following with more informed ones. First, an intensity-based matching algorithm is applied to the feature points to obtain unique point correspondences. This is followed by the application of a sequence of newly developed heuristic tests involving geometry, rigidity, and disparity. The geometric tests match two-dimensional geometrical relationships among the feature points, the rigidity test enforces the three dimensional rigidity of the object, and the disparity test ensures that no matched feature point in an image could be rematched with another feature, if reassigned another disparity value associated with another matched pair or an assumed match on the epipolar line. The computational complexity is proportional to the numbers of detected feature points in the two images. Experimental results with indoor and outdoor images are presented, which show that the algorithm yields only correct matches for scenes containing rigid objects.</p>
computational complexity; feature extraction; image sequences; geometry; point features; disparity constraints; geometric constraints; rigidity constraints; matching algorithm; feature detectors; intensity-based matching algorithm; heuristic tests; two-dimensional geometrical relationships; rigidity test; disparity test; epipolar line; computational complexity; indoor images

X. Hu and N. Ahuja, "Matching Point Features with Ordered Geometric, Rigidity, and Disparity Constraints," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 1041-1049, 1994.
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