A Bayesian multiple hypothesis approach to contour grouping
We present an approach to contour grouping based on classical tracking techniques. Edge points are segmented into smooth curves so as to minimize a recursively updated Bayesian probability measure. The resulting algorithm employs local smoothness constraints and a statistical description of edge detection, and can accurately handle corners, bifurcations, and curve intersections. Experimental results demonstrate good performance.
KeywordsFalse Alarm Edge Point Surveillance Region Parent Hypothesis Global Hypothesis
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