基于多目标人工鱼群算法的硅单晶直径检测图像阈值分割方法  

Monocrystalline Silicon Diameter Detection Image Threshold Segmentation Method Using Multi-objective Artificial Fish Swarm Algorithm

作　　者：刘丁[1,2] 张新雨[1,2] 陈亚军[1,2] 

机构地区：[1]西安理工大学晶体生长设备及系统集成国家地方联合工程研究中心,西安710048 [2]陕西省复杂系统控制与智能信息处理重点实验室,西安710048

出　　处：《自动化学报》2016年第3期431-442,共12页Acta Automatica Sinica

基　　金：国家自然科学基金重点项目(61533014);国家重点基础研究发展计划(973计划)(2014CB360508);高等学校博士学科点专项科研基金(20136118130001);陕西省自然科学基础研究计划项目(2013JQ8047;2014JM2-6111)资助~~

摘　　要：为提高对硅单晶直径检测图像高亮光环的分割精度,提出了一种基于多目标人工鱼群算法的二维直方图区域斜分多阈值分割方法.首先设计了一种多目标人工鱼群算法,并且改进了快速构造Pareto非劣解集的方法,然后以最大类间方差和最大熵同时作为测度函数,搜索最优的二维直方图区域斜分分割阈值.仿真结果表明,所设计的多目标人工鱼群优化算法具有较高的搜索精度,硅单晶直径检测图像分割实验结果表明,提出的改进二维直方图区域斜分多阈值分割方法对高亮光环具有较高的分割精度.Combined with the multi-objective artificial fish swarm algorithm（MAFSA）, a 2D histogram multi-threshold oblique segmentation method is proposed to improve the segmentation accuracy of the highlight halo for monocrystalline silicon diameter detection. Firstly, a multi-objective artificial fish swarm algorithm is designed and the approach to find the non-dominating set in a population efficiently is improved. Then, the maximum between-class variance and maximum entropy are simultaneously employed as measure functions to search the best thresholds of 2D histogram oblique segmentation. Simulated experiments show that the designed MAFSA has a relatively high search accuracy. Meanwhile,image segmentation experiments conducted on practical monocrystalline silicon diameter detection demonstrate that the proposed 2D histogram multi-threshold oblique segmentation method achieves fine precision on segmenting the highlight halo.

关 键 词：硅单晶直径检测 阈值分割 二维直方图区域斜分法 多目标优化 人工鱼群算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]