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.