Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling
Debora Gil
author
Petia Radeva
author
2003
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.
Initial condition
Convex shape
Non convex analysis
Increase
Segmentation
Gradient
Standard
Standards
Concave shape
Flow models
Tracking
Edge detection
Curvature
IAM;MILAB
exported from refbase (http://refbase.cvc.uab.es/show.php?record=1535), last updated on Fri, 11 Nov 2016 12:18:24 +0100
text
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5164
http://refbase.cvc.uab.es/files/GIR2003b.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5164
10.1007/978-3-540-45063-4_23
IAM @ iam @ GIR2003b
Energy Minimization Methods In Computer Vision And Pattern Recognition
LNCS
Springer
B.
editor
2003
Springer, Berlin
Lisbon, PORTUGAL
monographic
book
2683
357
372
3-540-40498-8
0302-9743
Lecture Notes in Computer Science
LNCS