@inproceedings{845ceaafd30a4bf5843d415dacea2594,
title = "A new point matching method based on position similarity",
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 = "Corresponding points matching, Euclid Distance, Evolutionary Programming, Position similarity",
author = "Pan, {Jun Jun} and Zhang, {Yan Ning}",
year = "2005",
language = "英语",
isbn = "078039092X",
series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",
pages = "5154--5158",
booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",
note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",
}