@inproceedings{83b649a06fd04095be34c5e030df9f32,
title = "Template Matching via Search History Driven Genetic Algorithm",
abstract = "Pixel-based template matching suffers from computational cost by increasing potential solutions. Genetic algorithms has been adopted to search hopeful solutions, whereas there is a demand for more accurate matching. In this paper, we propose to employ a modified real-coded genetic algorithm to solve the template matching problem. Specifically, individuals sampled during the exploration process are stored in an archive and spatially clustered in the search space. An enhanced crossover (abbreviated as SHX) exploits the extra cluster information to generate new individuals in more promising regions. To solve the matching problem, this algorithm searches for suitable geometric parameters using a pixel-level dense similarity measure. Experimental results show the effectiveness of SHX for solving the template matching problem.",
keywords = "Genetic algorithm, Search history, Template matching",
author = "Takumi Nakane and Takuya Akashi and Chao Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",
year = "2022",
doi = "10.1117/12.2624210",
language = "英語",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",
}