Abstract
Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do.
Highlights
Template matching is defined as the action of recognizing predefined template patterns in a source image
A source image and a predefined template image are superimposed in a certain location and the similarity evaluation is made on the basis of a selected model or criterion
All the simulations were implemented in MATLAB R2010a and executed on an Intel Core 2 Duo CPU with 2 GB RAM running at 2.53 GHz under Windows XP
Summary
Template matching is defined as the action of recognizing predefined template patterns in a source image. With regard to the search strategy for a best-match position, a thorough search algorithm is proposed as a pioneering work [10], in which all the pixel-candidate positions are checked until the one with the maximum similarity is located. Such exhaustive search is computationally expensive, which restricts its applications, especially in terms of some real-time recognition issues. Our previously proposed internal-feedback artificial bee colony (IF-ABC) algorithm is adopted as a search approach to find the best-match location for the template matching scheme. The final section includes the conclusions, the limitations of this study, and our future work
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