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论文范文
1. Introduction 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]. Template matching techniques involve the translation of the template to every possible position in the original image and the evaluation of the match between the template and the source image at that position [5, 6]. 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. On the other hand, feature-based method matches the basis of image feature such as border, unique point, texture, entropy, and energy [8]. 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]. In recent years, researchers have gradually shifted their interests towards the utilization of intelligent algorithms, such as artificial bee colony (ABC) [10], internal-feedback artificial bee colony (IFABC) [11], balance-evolution artificial bee colony (BEABC) [12], states of matter search (SMS) algorithm [13], and imperialist competitive algorithm (ICA) [7] which have been proposed for this template matching problem. Although these algorithms aim to reduce the computation load of the global optimums searching, none of them can completely guarantee the derivation of suboptimal matching results. It is important to note that most intelligent algorithms are generally suitable for the optimization of convex or nearly convex functions [11, 12]. However, the template matching field contains a finite number of feasible matching locations. Such a discrete function is not smooth, but extremely ill-conditioned actually. In other words, the solution surface may drastically oscillate along the domain, which critically restricts the advantages of such intelligent algorithms. Therefore, it needs a new way to modify these existing intelligent algorithms in order to accommodate the discreteness and oscillation in the objective function. In our paper, we utilize a recently proposed SFS, which was inspired by random fractals. SFS was firstly proposed by Salimi [14] who used such algorithm to solve the continuous optimization problems. By using the diffusion phenomenon that reveals fractals, SFS offers a new insight to solve complex optimization problems with fast convergence rate and high search accuracy. Because of these merits, SFS is attracting more and more attention and it has been successfully used for frame structure optimization problems [15, 16], target recognition [17], precise trajectory optimization [18], distributed database queries optimization [19], and so on. Moreover, LI approach is utilized to preprocess the original and template images before using SFS for matching. The hybrid method is named LI-SFS. Lateral inhibition mechanism is firstly discovered and verified by Hartline and his research group when they conducted an electrophysiology experiment on the limulus’ vision [20]. This mechanism, which provides great help to detect object in cluttered background, can enhance the contrast of sensory information and reduce low-frequency noises [10]. Experimental results confirm that SFS is more capable than several intelligent algorithms such as IFABC [11], BEABC [12], SMS [13], and ICA [7] in this LI-based template matching scheme. The rest of the paper is structured as follows. Section 2 discusses the principles of SFS. Section 3 describes lateral inhibition mechanism. Section 4 details the implementation procedures of the hybrid method LI-SFS. Then, several cases of comparative experiments have been conducted in Section 5. Finally, Section 6 concludes the study and advises some directions for future studies. 
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