Liu Xinran, Wang Zhongju, Wang Long, Huang Chao, Luo Xiong
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Shunde Graduate School, University of Science and Technology Beijing, Foshan 528300, China.
Entropy (Basel). 2021 May 27;23(6):678. doi: 10.3390/e23060678.
This paper proposes a hybrid Rao-Nelder-Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.
本文提出了一种用于图像模板匹配的混合Rao-Nelder-Mead（Rao-NM）算法。所开发的算法将Rao-1算法和NM算法串行结合。因此，充分利用了Rao-1算法强大的全局搜索能力和NM算法的局部搜索能力。它能够在确保全局收敛的基础上快速准确地搜索到高质量的最优解。计算时间大幅减少，同时匹配精度显著提高。采用四个常用的优化问题和三个图像数据集来评估所提方法的性能。同时，将三种常用算法，包括通用Rao-1算法、粒子群优化（PSO）算法、遗传算法（GA），作为基准算法。实验结果表明，所提方法在解决图像匹配问题方面是有效且高效的。
