%0 Book Section
%T Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling
%A Debora Gil
%A Petia Radeva
%E Springer, B.
%B Energy Minimization Methods In Computer Vision And Pattern Recognition
%D 2003
%V 2683
%I Springer, Berlin
%C Lisbon, PORTUGAL
%@ 0302-9743
%@ 3-540-40498-8
%F Debora Gil2003
%O IAM;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1535), last updated on Fri, 11 Nov 2016 12:18:24 +0100
%X Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time.
%K Initial condition
%K Convex shape
%K Non convex analysis
%K Increase
%K Segmentation
%K Gradient
%K Standard
%K Standards
%K Concave shape
%K Flow models
%K Tracking
%K Edge detection
%K Curvature
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5164
%U http://refbase.cvc.uab.es/files/GIR2003b.pdf
%U http://dx.doi.org/10.1007/978-3-540-45063-4_23
%P 357-372