In order to achieve correct perspective template matching on two‐dimensional general images, we propose a differential evolution (DE) introducing the group remaking. Since the perspective template matching has eight degree of freedom (DoF), acquisition of a global optimum, which is an area corresponding to a template in a target image, within limited calculation resource is difficult. In order to address this task, DE is utilized. This method can optimize some parameters simultaneously and acquire a good approximate solution within the limited calculation resources. The adopted DE divides the population into two equal groups, exploitation group for local search and exploration group for global search. By remaking the exploitation group every time, the best solution is found and updated, more effective search is possible. Also, linear group size changing scheme and initialization of parameters for crossover and mutation are introduced to acquire good performance. The experimental results using a general 2D image dataset show the lowest average error of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

中文翻译：

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通过差异演化匹配透视图模板，引入团队重构

为了在二维普通图像上实现正确的透视模板匹配，我们提出了一种引入组重构的差分进化（DE）方法。由于透视图模板匹配具有八个自由度（DoF），因此难以在有限的计算资源内获取全局最优值，该全局最优值是与目标图像中的模板相对应的区域。为了解决该任务，利用了DE。该方法可以同时优化一些参数，并在有限的计算资源内获得良好的近似解。所采用的DE将人口分为两个相等的组，用于本地搜索的开发组和用于全局搜索的探索组。通过每次重新构建开发组，可以找到并更新最佳解决方案，从而可以进行更有效的搜索。也，介绍了线性组大小更改方案以及用于交叉和变异的参数初始化，以获得良好的性能。使用常规2D图像数据集的实验结果显示了所提出方法的最低平均误差。©2020日本电气工程师学会。由John Wiley＆Sons，Inc.发布