Improved matching point purification algorithm mRANSAC

Yawei Wang, Tingfa Xu*, Jihui Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To sove the problem that correct match points cannot be effectively extracted in the match point purification links of current image matching algorithms, mRANSAC (multi-RANSAC) multi-transformation matrix method is proposed. Matching points cannot accurately correspond to each other due to the digital image's discrete sample style. There exist intrinsic position errors, and the corresponding transform matrices are different. Therefore, one transformation matrix cannot contain all the correctly matched points. The research results of RANSAC show that in the sets of non-maximal inner point number, enough points can induce correct matches, which can also be confirmed by the fact that different objective images result in different match numbers. Therefore, multi-transformation matrix is used to increase the matching point number and improve the purification efficiency. Three strategies, the set union method, the set extract method and the adaptive number threshold of inner point method are proposed. The purification results of mRANSAC are generally 60% to 300% more than those of RANSAC. Through setting suitable threshold value of mRANSAC, the purification rate can reach approximately 100%. This method can also be applied to solve the similar purification problem in other fields.

Original languageEnglish
Pages (from-to)163-167
Number of pages5
JournalDongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition)
Volume43
Issue numberSUPPL.I
DOIs
Publication statusPublished - Jul 2013

Keywords

  • Image matching
  • Matching point purification
  • RANSAC (random sample consensus)
  • Transformation matrix

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