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Active Curve Recovery of Region Boundary Patterns

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3 Author(s)
Ben Salah, M. ; Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB, Canada ; Ben Ayed, I. ; Mitiche, A.

This study investigates the recovery of region boundary patterns in an image by a variational level set method which drives an active curve to coincide with boundaries on which a feature distribution matches a reference distribution. We formulate the scheme for both the Kullback-Leibler and the Bhattacharyya similarities, and apply it in two conditions: the simultaneous recovery of all region boundaries consistent with a given outline pattern, and segmentation in the presence of faded boundary segments. The first task uses an image-based geometric feature, and the second a photometric feature. In each case, the corresponding curve evolution equation can be viewed as a geodesic active contour (GAC) flow having a variable stopping function which depends on the feature distribution on the active curve. This affords a potent global representation of the target boundaries, which can effectively drive active curve segmentation in a variety of otherwise adverse conditions. Detailed experimentation shows that the scheme can significantly improve on current region and edge-based formulations.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 5 )

Date of Publication:

May 2012

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