A novel point pattern matching algorithm based on point feature is proposed. In the paper, we construct the point's feature map, according to the point set's distribution and points' position. Then the log-polar coordinate transformation is applied to the feature map, and the moment invariants method is used to describe the transformed feature map and it's written by the form of vectors. Thus, the curse matching results is acquired by comparing the feature vectors. After these, an iterative method,the relaxation labeling method, is introduced for the final matching result. There are two contributions made in this paper. Firstly, we construct a log-polar coordinate transformation based point feature(L-PTM), which can stand affine transformation.Secondly, a new point pattern matching algorithm is proposed, which is combined L-PTM with the relaxation labeling. The method is insensitive to outliers and noises. Experiments demonstrate the validity and robustness of the algorithm.

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090T (8 March 2018); doi: 10.1117/12.2284970

Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)