Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
