DocumentCode :
442204
Title :
A new point matching method based on position similarity
Author :
Pan, Jun-Jun ; Zhang, Yan-ning
Author_Institution :
Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi´´an, China
Volume :
8
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
5154
Abstract :
A new corresponding point matching method is presented when studying the feature points matching from X-ray images, which is called "the regulation of the minimum summation of Euclid distance". This method is different from the conventional matching approaches based on gray level or based on region geometric feature. It is based on the position similarity of corresponding points. This method derives from the model of sequence matching algorithm. According to the condition that the relative position of any two points in adjacent area from two correlative images are almost constant, this method minimizes the summation of corresponding points distance by adjusting the sequence of points through evolutionary programming searching. The experimental result shows that this method can match the most feature points correctly in low time-consuming, just based on the position similarity of points.
Keywords :
X-ray imaging; evolutionary computation; feature extraction; image matching; image sequences; minimisation; search problems; Euclid distance summation; X-ray image; evolutionary programming searching; feature points matching; minimization; point matching; position similarity; sequence matching algorithm; Corresponding points matching; Euclid Distance; Evolutionary Programming; Position similarity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527852
Filename :
1527852
Link To Document :
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