CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.05 - May
Issue No.05 - May (2012 vol.34)
Ismail Ben Ayed , University of Western Ontario, London and General Electric Canada, London
Mohamed Ben Salah , Institut National de la Recherche Scientifique, Montreal and University of Alberta, Edmonton
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.
Image segmentation, boundary patterns, boundary feature distributions, active curves, level sets, similarity measures.
Ismail Ben Ayed, Mohamed Ben Salah, "Active Curve Recovery of Region Boundary Patterns", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 5, pp. 834-849, May 2012, doi:10.1109/TPAMI.2011.201