Abstract
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.
The authors would like to thank Y. Bar-Shalom, H. Durrant-Whyte, T. Kurien and J. J. Leonard for valuable discussion on issues related to target tracking.
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© 1992 Springer-Verlag Berlin Heidelberg
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Cox, I.J., Rehg, J.M., Hingorani, S. (1992). A Bayesian multiple hypothesis approach to contour grouping. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_9
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DOI: https://doi.org/10.1007/3-540-55426-2_9
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