Efficient object matching using affine-invariant deformable contour

Zhong Xue, Stan Z. Li, Eam Khwang Teoh

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

An affine-invariant deformable contour model for object matching, called AI-EigenSnake (AI-ES), is presented in the Bayesian framework. In AI-ES, the prior distribution of object shapes is estimated and utilized to constrain the prototype contour, which is dynamically adjustable in the matching process. Also, an affine-invariant internal energy is presented to define the global and local shape deformation of the contours between the shape domain and the image domain. Experiments on real object matching show that the proposed method is more robust and insensitive to the positions, viewpoints, and large deformations of object shapes, as compared with the Active Shape Model (ASM) and the AI-Snake model.

Original languageEnglish (US)
Pages (from-to)672-675
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume15
Issue number1
StatePublished - Dec 1 2000

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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