Efficient template matching for multi-channel images

Template matching is a computationally intensive problem aimed at locating a template within a image. When dealing with images having more than one channel, the computational burden becomes even more dramatic. For this reason, in this paper we investigate on a methodology to speed-up template matching on multi-channel images without deteriorating the outcome of the search. In particular, we propose a fast, exhaustive technique based on the Zero-mean Normalized Cross-Correlation (ZNCC) inspired from previous work related to grayscale images. Experimental testing performed over thousands of template matching instances demonstrates the efficiency of our proposal.

Research highlights
► A methodology to speed-up template matching on multi-channel images without approximating the outcome of the search is investigated.
► A fast, exhaustive technique based on the Zero-mean Normalized Cross-Correlation (ZNCC) inspired from previous work related to grayscale images is proposed.
► An extensive experimental validation of the proposed algorithm is performed over 9000 template matching instances, demonstrating the effectiveness of our proposal.