Abstract
Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.
Highlights
Template matching is a way of getting a measure of similarity between two image sets that are superimposed on one another
We verify the performance of our proposed algorithm through a series of comparative experiments with those produced by the internal-feedback artificial bee colony (IFABC) method [11], balance-evolution artificial bee colony (BEABC) method [12], states of matter search (SMS) method [13], and imperialist competitive algorithm (ICA) method [7]
Simulations have been executed across seven images 1–7 that are shown in Figures 3–9, respectively
Summary
Template matching is a way of getting a measure of similarity between two image sets that are superimposed on one another. It is a hot issue in the field of face recognition [1], pulmonary nodules detection [2], handwriting identification [3], and road detection [4]. The existing template matching techniques can be divided into two categories: the intensity-based approach and the feature-based approach [7]. The intensity-based method can be regarded as an optimization process of finding the maximum similar degree between the template and the original image. Compared with the feature-based method, the intensity-based method provides better performance and is widely used as it is independent of extensive feature extractions and has superior ability to restrain noise [9]
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