%0 Journal Article
%T A Regularized Curvature Flow Designed for a Selective Shape Restoration
%A Debora Gil
%A Petia Radeva
%J IEEE Transactions on Image Processing
%D 2004
%V 13
%F Debora Gil2004
%O IAM;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=491), last updated on Fri, 11 Nov 2016 12:17:30 +0100
%X Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.
%K Geometric flows
%K nonlinear filtering
%K shape recovery.
%U http://refbase.cvc.uab.es/files/GiR2004b.pdf
%U http://dx.doi.org/10.1109/TIP.2004.836181
%P 1444–1458