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A BAYESIAN MULTIPLE-HYPOTHESIS APPROACH TO EDGE GROUPING AND CONTOUR SEGMENTATION

COX, IJ; REHG, JM; HINGORANI, S; (1993) A BAYESIAN MULTIPLE-HYPOTHESIS APPROACH TO EDGE GROUPING AND CONTOUR SEGMENTATION. INT J COMPUT VISION , 11 (1) 5 - 24.

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Abstract

A contour segmentation algorithm is presented that takes an edge map and extracts continuous curves of arbitrary smoothness, correctly handling curve intersections and capable of extrapolating over significant measurement gaps. The algorithm incorporates noise models of the edge-detection process and limited scene statistics. It is based on an explicit contour model and employs a statistical distance measure to quantify the likelihood of each segmentation hypothesis. A Bayesian multiple-hypothesis tree organizes possible segementations, making it possible to postpone grouping decisions until a sufficient amount of information is available. We have demonstrated its performance on real and synthetic images.

Type: Article
Title: A BAYESIAN MULTIPLE-HYPOTHESIS APPROACH TO EDGE GROUPING AND CONTOUR SEGMENTATION
Keywords: IMAGE, OBJECTS, RECOGNITION, ALGORITHMS
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1300773
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