Open AccessShape matching and object recognition using common base triangle area
Author(s) -
Hu Dameng,
Huang Weiguo,
Yang Jianyu,
Shang Li,
Zhu Zhongkui
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0409
Subject(s) - artificial intelligence , matching (statistics) , pattern recognition (psychology) , computer vision , computer science , similarity (geometry) , dynamic programming , point (geometry) , mathematics , base (topology) , cognitive neuroscience of visual object recognition , basis (linear algebra) , object (grammar) , algorithm , image (mathematics) , geometry , mathematical analysis , statistics
Shape matching has always been a key issue in the field of computer vision. To obtain high recognition accuracy with low time complexity and to reduce the influence of contour deformation due to noise in shape matching, a novel shape matching method based on common base triangle area (CBTA) is proposed. First, a CBTA descriptor of each contour point is defined based on the area functions of the triangles formed by its two neighbour points and other contour points. Then, the descriptor is locally smoothed to keep it more compact and robust to noise. Secondly, a match cost matrix is obtained by computing the CBTA descriptors of all the contour points on two shapes. Finally, the similarity between the two shapes is measured on the basis of the match cost matrix by a dynamic programming algorithm. The experimental results on MPEG‐7, Kimia and an articulation shape database indicate that this method is robust to contour deformation, and both the computational efficiency and the retrieval rate are essentially improved.