A new point matching method based on position similarity

Jun Jun Pan, Yan Ning Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages5154-5158
Number of pages5
StatePublished - 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period18/08/0521/08/05

Keywords

  • Corresponding points matching
  • Euclid Distance
  • Evolutionary Programming
  • Position similarity

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