Point-pattern matching method using SURF and Shape Context

Point-pattern matching is an important topic in the fields of computer vision and pattern recognition. We present a novel point-pattern matching method based on Speeded Up Robust Feature (SURF) and Shape Context to increase the matching accuracy. In the original point-pattern matching method using SURF algorithm, incorrect matching point pairs are likely to be produced and are difficult to be eliminated only by using information of image regions around the feature points. In the proposed method, firstly, SURF bidirectional matching method is applied to match the feature points in two images preliminarily. Then Shape Context descriptors are calculated for the feature points so that the information of relative positions among the feature points can be integrated in this way. Finally, incorrect matching point pairs can be eliminated gradually by an iteration method. Experiment results show that the proposed method can eliminate incorrect matching point pairs effectively and increase the accuracy of point-pattern matching.