Active curve recovery of region boundary patterns

Active curve recovery of region boundary patterns

Salah, Mohamed Ben and Ayed, Ismail Ben and Mitiche, Amar

IEEE Transactions on Pattern Analysis and Machine Intelligence 2012

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. © 2012 IEEE.