SIFT feature point matching based on improved RANSAC algorithm

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

64 Citations (Scopus)

Abstract

When matching the SIFT feature points, there will be lots of mismatches. The RANSAC algorithm can be used to remove the mismatches by finding the transformation matrix of these feature points. But when the data space contains a lot of mismatches, finding the right transformation matrix will be very difficult. What's more, the probability of finding the error model is very large. Aiming at solving the problem, this paper proposed an improved RANSAC algorithm. Before using the RANSAC algorithm, we removed parts of the error feature points by two methods, one is eliminating features not belonging to the target area and the other is removing the crossing points. The two methods aimed to improve the proportion of feature points matched correctly. Experiments showed that, the improved RANSAC algorithm could find the model more accurately, improve efficiency, and make the feature point matching more accurately.

Original languageEnglish
Title of host publicationProceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
Pages474-477
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013 - Hangzhou, Zhejiang, China
Duration: 26 Aug 201327 Aug 2013

Publication series

NameProceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
Volume1

Conference

Conference2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
Country/TerritoryChina
CityHangzhou, Zhejiang
Period26/08/1327/08/13

Keywords

  • Improved RANSAC
  • Key point matching
  • SIFT

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