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Author Debora Gil; Petia Radeva
Title Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling Type Book Chapter
Year 2003 Publication Energy Minimization Methods In Computer Vision And Pattern Recognition Abbreviated Journal LNCS
Volume 2683 Issue Pages 357-372
Keywords Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature
Abstract 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.
Corporate Author Thesis
Publisher Springer, Berlin Place of Publication Lisbon, PORTUGAL Editor Springer, B.
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 3-540-40498-8 Medium (up)
Area Expedition Conference
Notes IAM;MILAB Approved no
Call Number IAM @ iam @ GIR2003b Serial 1535
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