Abstract

In many image processing applications, the Template Matching (TM) technique plays an important role in recognizing and locating patterns or objects within a digital image. The main task of the TM is to seeks and find a position within an original image that resembles a predetermined sub-image (template) and a corresponding region of the original image as much as possible. TM involves two main aspects: similarity measurement and search strategy. The simplest method applied to TM involves a comprehensive calculation of the value of the Normalized Cross-Correlation (NCC) over all the pixel locations of the source image (search strategy). Unfortunately, the high computational cost that implies the evaluation of the NCC coefficient makes this approach restricted. We propose several TM methods based on evolutionary approaches as an alternative to reduce the number of search locations in the TM process. We have conducted a comparison of several evolutionary methods, to obtain which of these is the optimum to perform the TM task. Experimental results of this comparison show us which methods achieve the best balance between estimation and computational cost.

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