Image alignment using learning prior appearance model

被引：11
作者：
El-Baz, Ayman [1 ]
Farag, Aly [1 ]
Gimel'fiarb, Georgy [2 ]
Abdel-Hakim, Alaa E. [1 ]
机构：
[1] Univ Louisville, CVIP Lab, Louisville, KY 40292 USA
[2] Univ Auckland, Dept Comp Sci, Auckland 1, New Zealand
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS | 2006年
关键词：
Markov Gibbs random field; appearance model; registration;
D O I：
10.1109/ICIP.2006.313163
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
引用
收藏
页码：341 / +
页数：2
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