Novel eigenvector approach to pose and correspondence estimation

Zhong Xue, Eam Khwang Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper proposes a novel eigenvector approach for pose and correspondence estimation between the feature points of two images or two point patterns under affine transformation. In the method, the proximity matrices, which record the normalized area features extracted from the two point sets are utilized to calculate the modes of each point set and the corresponding feature vectors. Then the point correspondence can be obtained by calculating the correlation of the feature vectors. To reduce the computation time, the idea of the principal component analysis (PCA) is adopted, which considers only the principal eigenvectors corresponding to the larger eigenvalues of each proximity matrix. As compared with the traditional eigenvector algorithm proposed by Shapiro and Brady, the proposed algorithm is demonstrated to be more effective in estimating the point correspondence and the relevant parameters of affine transformation.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1489-1494
Number of pages6
Volume2
StatePublished - 2000
Event2000 IEEE Interantional Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: Oct 8 2000Oct 11 2000

Other

Other2000 IEEE Interantional Conference on Systems, Man and Cybernetics
CityNashville, TN, USA
Period10/8/0010/11/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

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