Image Matching Algorithm based on ORB and K-Means Clustering

  • Liye Zhang
  • , Fudong Cai
  • , Jinjun Wang
  • , Changfeng Lv
  • , Wei Liu
  • , Guoxin Guo
  • , Huanyun Liu
  • , Yixin Xing

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

8 Scopus citations

Abstract

With the rapid development of science and technology, image processing technology plays an important role in the field of computer vision. In order to improve the matching speed and real-time requirements, this paper proposes an image matching algorithm based on ORB and K-means clustering, which can effectively improve the accuracy of image feature point location and the accuracy and efficiency of image feature matching, and reduce the time consumption. The algorithm uses sub-pixel interpolation to optimize the traditional ORB algorithm, which improves the accuracy and characteristics of clustering calculation.

Original languageEnglish
Title of host publicationProceedings - 2020 5th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-464
Number of pages5
ISBN (Electronic)9781728185750
DOIs
StatePublished - Nov 2020
Event5th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2020 - Shenyang, China
Duration: 13 Nov 202015 Nov 2020

Publication series

NameProceedings - 2020 5th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2020

Conference

Conference5th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2020
Country/TerritoryChina
CityShenyang
Period13/11/2015/11/20

Keywords

  • K-means
  • ORB
  • binocular stereo vision
  • image matching
  • sub-pixel interpolation

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