title: Active curve recovery of region boundary patterns.
abstract: 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.
authors.affiliation: Department of Computing Science, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta T6G 2E8, Canada. bensala@ualberta.ca
authors.forename: Mohamed
authors.initials: M
authors.lastname: Ben Salah
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.forename: Amar
authors.initials: A
authors.lastname: Mitiche