An Eigenvector Approach Based on Shape Context Patterns for Point Matching
Xiabi Liu, Yunde Jia, Yanjie Wang · 2006
In this paper, the problem of point correspondence across two images is treated in the eigenvector analysis matching framework of Scott and Longuet-Higgins. We develop the concept of shape contexts introduced by S. Belongie et al. to shape context patterns as rich local descriptors of points. We further propose a Gaussian-weighted Hausdorff distance between shape context patterns to measure correspondence strength in Scott and Longuet-Higgins framework. The resultant point matching approach is applied to estimate affine transformation between handwritten Chinese character images, whose effectiveness is confirmed by the experimental results