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

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.

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

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.