Paper Info
Title
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother
Abstract
In this paper, we present a new non-parametric combined segmentation and registration method. The problem is cast as an optimization one, combining a matching criterion based on the active contour without edges [4] for segmentation, and a nonlinear-elasticity-based smoother on the displacement vector field. This modeling is twofold: first, registration is jointly performed with segmentation since guided by the segmentation process; it means that the algorithm produces both a smooth mapping between the two shapes and the segmentation of the object contained in the reference image. Secondly, the use of a nonlinear-elasticity-type regularizer allows large deformations to occur, which makes the model comparable in this point with the viscous fluid registration method [7]. Several applications are proposed to demonstrate the potential of this method to both segmentation of one single image and to registration between two images.
Year
DOI
Venue
2011
10.1007/978-3-642-02256-2_50
Computer Vision and Image Understanding
Keywords
Field
DocType
active contour,calculus of variation,image segmentation,image registration,viscous fluid,augmented lagrangian,level set,calculus of variations
Nonlinear elasticity,Active contour model,Computer vision,Scale-space segmentation,Segmentation,Reference image,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Mathematics,Displacement (vector)
Journal
Volume
Issue
ISSN
115
12
0302-9743
Citations 
PageRank 
References 
9
0.60
27
Authors
2
Name
Order
Citations
PageRank
carole le guyader112614.01
Luminita A. Vese25389302.64