Invariant feature matching for image registration application based on new dissimilarity of spatial features

Seyed Mostafa Mousavi Kahaki, Md. Jan Nordin, Amir H. Ashtari, Sophia J. Zahra

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics - such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient - are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

Original languageEnglish
Article numbere0149710
JournalPLoS One
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Mar 2016

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Image registration
Eigenvalues and eigenfunctions
methodology
Politics

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Invariant feature matching for image registration application based on new dissimilarity of spatial features. / Mousavi Kahaki, Seyed Mostafa; Nordin, Md. Jan; Ashtari, Amir H.; Zahra, Sophia J.

In: PLoS One, Vol. 11, No. 3, e0149710, 01.03.2016.

Research output: Contribution to journalArticle

Mousavi Kahaki, Seyed Mostafa ; Nordin, Md. Jan ; Ashtari, Amir H. ; Zahra, Sophia J. / Invariant feature matching for image registration application based on new dissimilarity of spatial features. In: PLoS One. 2016 ; Vol. 11, No. 3.
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