Fast and robust template matching with majority neighbour similarity and annulus projection transformation.

• In this paper, we propose a novel fast and robust template matching method named A-MNS based on the Most Neighbors Similarity (MNS) and the annulus projection transformation (APT). The proposed A-MNS is able to estimate the rotation angle of the target object, overcome the challenges such as background clutter, occlusion, arbitrary rotation transformation, non-rigid deformation and perform fast matching. • A-MNS is efficient due to the coarse-to-fine matching strategy and MNS measurement which avoids the sliding window scan. The coarse matching stage utilizes a low-cost feature APT vector to obtain the matching candidates, and then uses MNS to provide an accurate match. • The essential of A-MNS is the MNS, a useful, rotation invariant, low computational cost and robust similarity measurement. It considers the global spatial structure of the object via counting the quantity of relative neighbours. In the paper, a novel fast and robust template matching method named A-MNS based on Majority Neighbour Similarity (MNS) and the annulus projection transformation (APT) is proposed. Its essence is the MNS, a useful, rotation-invariant, low-computational-cost and robust similarity measurement. The proposed method is theoretically demonstrated and experimentally evaluated as being able to estimate the rotation angle of the target object, overcome challenges such as background clutter, occlusion, arbitrary rotation transformation, and non-rigid deformation, while performing fast matching. Empirical results evaluated on the up-to-date benchmark show that A-MNS is 4.419 times faster than DDIS (the state-of-the-art) and is also competitive in terms of its matching accuracy. [ABSTRACT FROM AUTHOR]