Feature point matching based on ABC-NCC algorithm

Abstract

Feature point matching is the process of finding an optimal spatial transformation that aligns two arbitrary sets of feature points. It is one of the most fundamental problems in the computer vision domain and is frequently used in object recognition, image registration, camera self-calibration, and so on. Critical to most feature point matching techniques is the determination of correspondence between spatially localized feature points within each image. Moreover, there can be many feature points in either set that have no counterparts in the other. A robust and effective method for feature point matching is thus required and is still a challenge. In this work, an artificial bee colony (ABC) with a normalized cross-correlation (NCC) algorithm called “ABC-NCC” for feature point matching is presented. In this proposed method, both the size and the orientation of the correlation window used for calculating the NCC are determined according to the scale and the rotation direction of the interest points, which are optimized by the ABC algorithm. Experimental results obtained by our method show that the proposed approach works well for feature point matching and outperforms existing algorithms.