TY - CHAP
AU - Debora Gil
AU - Petia Radeva
ED - Springer, B.
PY - 2003//
TI - Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling
T2 - LNCS
BT - Energy Minimization Methods In Computer Vision And Pattern Recognition
T3 - Lecture Notes in Computer Science
SP - 357
EP - 372
VL - 2683
PB - Springer, Berlin
CY - Lisbon, PORTUGAL
KW - Initial condition
KW - Convex shape
KW - Non convex analysis
KW - Increase
KW - Segmentation
KW - Gradient
KW - Standard
KW - Standards
KW - Concave shape
KW - Flow models
KW - Tracking
KW - Edge detection
KW - Curvature
N2 - 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.
SN - 0302-9743
SN - 3-540-40498-8
UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5164
L1 - http://refbase.cvc.uab.es/files/GIR2003b.pdf
UR - http://dx.doi.org/10.1007/978-3-540-45063-4_23
N1 - IAM;MILAB
ID - Debora Gil2003
ER -