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<p><b>Abstract</b>—The interpretation of the 3D world from image sequences requires the identification and correspondences of key features in the scene. In this paper, we describe a robust algorithm for matching groupings of features related to the objects in the scene. We consider the propagation of uncertainty from the feature detection stage through the grouping stage to provide a measure of uncertainty at the matching stage. We focus upon indoor scenes and match junctions, which are groupings of line segments that meet at a single point. A model of the uncertainty in junction detection is described, and the junction uncertainty under the epipolar constraint is determined. Junction correspondence is achieved through matching of each line segment associated with the junction. A match likelihood is then derived based upon the detection uncertainties and then combined with information on junction topology to create a similarity measure. A robust matching algorithm is proposed and used to match junctions between pairs of images. The presented experimental results on real images show that the matching algorithm produces sufficiently reliable results for applications such as structure from motion.</p>
Feature tracking, junction, uncertainty propagation, junction matching, junction topology, topological matching, epipolar geometry.

P. Palmer and X. Shen, "Uncertainty Propagation and the Matching of Junctions as Feature Groupings," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 1381-1395, 2000.
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