Affine template matching by differential evolution with adaptive two‐part search

摘要

In this paper, we address the affine template matching of general images. The extensive search space of affine transformations necessitates effective searches of the global optimum. The proposed method utilizes differential evolution (DE), which is a method of metaheuristic optimization, to achieve that goal. Self‐adaptive DEs can be useful and are applicable in a wide range of studies as they tune crossover rate and scaling factor (F) themselves over generation iteration. However, this approach is not particularly good for affine template matching because the population often converges to local optima. In order to solve this problem, the population is divided into two equal groups for exploitation and exploration. The former group utilizes current‐to‐best/1, and the latter group adopts improved current‐to‐rand/1 for the mutation scheme. Furthermore, the proportion of the population sizes of the two groups are linearly changed on the basis of the best sum of absolute difference error measurements over each generation. These ideas are easy and simple, but experimental results have revealed our method to be more accurate than the state‐of‐the‐art method. ? 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.