Efficient point pattern matching algorithm for planar point sets under transform of translation, rotation and scale

Abstract

Point pattern matching is an important topic in computer vision and pattern recognition, and finds many applications such as image registration, motion detection, object tracking and pose estimation. In this paper, we propose an efficient algorithm for determining correspondence between two planar point sets under transform of translation, rotation and scale. This algorithm randomly selects some points of a set and extracts their neighbor points. It views the selected points and their neighbor points as local point patterns, and finds the local matched patterns in the other set. Point pattern matching is finally achieved by counting the unique point number of those local matched point patterns with the same transform parameters. Many experiments are conducted to validate efficiency of the proposed algorithm. Running time comparisons with a well-known point pattern matching algorithm are also done and the results show that the proposed algorithm is faster than the compared algorithm.