access icon free Shape matching algorithm based on shape contexts

This study proposes a novel shape matching algorithm through exploiting shape contexts. The contributions of the proposed algorithm are twofold: (i) a new framework is presented to deal with the shape matching problem based on shape contexts, but differently from existing methods, the authors exploit a polynomial fitting-based feature point extraction method as a preprocessing step, so as to enhance the performance of the shape contexts-based descriptor; (ii) the authors design a voting classification method based on the chi-square statistical measure to evaluate the matching results. The experimental results show that this method is able to achieve high performance, even if shapes of testing objects suffer from translation, rotation and scaling.

Inspec keywords: shape recognition; polynomials; computer vision; statistical analysis; image matching; feature extraction; object recognition

Other keywords: shape contexts based descriptor; object recognition; computer vision; polynomial fitting based feature point extraction method; voting classification method; shape matching problem; shape matching algorithm; chi-square statistical measure

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Computer vision and image processing techniques; Algebra; Algebra; Image recognition; Interpolation and function approximation (numerical analysis); Other topics in statistics

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