| 基于麻雀算法优化的OSTU分割算法 |
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| 引用本文： | 李鹏,丁倩雯. 基于麻雀算法优化的OSTU分割算法[J]. 电子测量技术, 2021, 44(19): 148-154 |
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| 作者姓名： | 李鹏 丁倩雯 |
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| 作者单位： | 1.南京信息工程大学 江苏省气象探测与信息处理重点实验室 南京 210044;2.南京信息工程大学 江苏省大气环境与装备技术协同创新中心 南京 210044;3 南京信息工程大学 滨江学院 无锡 214105 |
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| 基金项目： | 江苏省重点研发计划社会发展项目(BE2015692)、江苏省第11批六大高峰人才项目(2014-XXRJ-006)资助 |
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| 摘 要： | 针对传统最大类间差法(OSTU)在分割图像时计算量大、时间效率低的缺点,提出一种基于Singer混沌映射和随机游走策略的麻雀优化的OSTU分割方法(SRWSSA).首先,利用Singer混沌映射改进初始化麻雀种群,增加初始麻雀种群的多样性,提高全局搜索能力;其次,采用随机游走策略对更新后的最优麻雀进行扰动变异,进一步增...
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| 关 键 词： | 麻雀搜索算法 图像分割 OSTU算法 群智能优化算法 |
OSTU segmentation algorithm based on sparrow algorithm optimization |
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| Li Peng,Ding Qianwen. OSTU segmentation algorithm based on sparrow algorithm optimization[J]. Electronic Measurement Technology, 2021, 44(19): 148-154 |
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| Authors: | Li Peng Ding Qianwen |
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| Affiliation: | 1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ;3.Binjiang College,Nanjing University of Information Science and Technology, Wuxi 214105, China |
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| Abstract: | Aiming at the disadvantages of large amount of calculation and low time efficiency of traditional maximum inter class difference method (OSTU) in image segmentation, a sparrow optimized OSTU segmentation method (SRWSSA) based on singer chaotic map and random walk strategy is proposed. Firstly, singer chaotic map is used to improve the initialization sparrow population, increase the diversity of initialization population and improve the global search ability; Secondly, the random walk strategy is used to perturb and mutate the updated optimal sparrow, so as to further increase the population diversity and enhance the local search ability; Finally, the standard image is segmented by two-dimensional OSTU using the proposed optimization algorithm to obtain the optimal threshold segmentation image. The SRWSSA algorithm proposed in this paper has significantly improved the optimization ability and iteration time. Compared with PSO-OSTU and SSA-OSTU, the number of iterations is reduced by 83.3% and 76% respectively. The image peak signal-to-noise ratio is increased by 8.2% and 11.3% respectively, and the running time is also improved. Practice shows that this method is feasible. |
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| Keywords: | Sparrow search algorithm Image segmentation OSTU algorithm Swarm intelligence optimization algorithm |
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