Affine-invariant shape matching algorithm based on modified multi-scale product Laplacian of Gaussian operator
DU Haijing1,2,XIAO Yanghui1,2,3,ZHU Dan1,2,3,TONG Xinxin1,2,3
1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;
3. Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang Liaoning 110016, China
Geometric transforms of the object in the imaging process can be represented by affine transform in most situations. Therefore, a method for shape matching using corners was proposed. Firstly, the corner of contour using Multi-scale Product Laplacian of Gaussian (MPLoG) operator was detected, and the feature points based on corner interval were adaptively extracted to obtain the key feature of shape. In order to cope with affine transform, the similarity of two shapes on Grassmann manifold Gr(2,n) were represented and measured. Finally, the iterative sequence shift matching was adopted for overcoming the dependency of Grassmann manifold on the starting point, and achieving shape matching. The proposed algorithm was tested on the database of shapes. The simulation results show that the proposed method can achieve shape recognition and retrieval effectively, and it has strong robustness against noise.