Non-rigid registration using distance functions

被引:101
|
作者
Paragios, N
Rousson, M
Ramesh, V
机构
[1] Siemens Corp Res, Imaging & Visualizat Dept, Princeton, NJ 08540 USA
[2] INRIA, F-06902 Sophia Antipolis, France
关键词
distance functions; shape matching; level set representations; variational methods; sum of squared differences; joint registration and learning;
D O I
10.1016/S1077-3142(03)00010-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper deals with the registration of geometric shapes. Our primary contribution is the use of a simple and robust shape representation (distance functions) for global-to-local alignment. We propose a rigid-invariant variational framework that can deal as well with local nonrigid transformations. To this end, the registration map consists of a linear motion model and a local deformations field, incrementally recovered. In order to demonstrate the performance of the selected representation a simple criterion is considered, the sum of square differences. Empirical validation and promising results were obtained on examples that exhibit large global motion as well as important local deformations and arbitrary topological changes. (C) 2003 Elsevier Science (USA). All rights reserved.
引用
收藏
页码:142 / 165
页数:24
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