| 基于反向萤火虫算法的多阈值缺陷图像分割(英文) |
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| 引用本文： | 陈恺,戴敏,张志胜,陈平,史金飞.基于反向萤火虫算法的多阈值缺陷图像分割(英文)[J].东南大学学报,2014(4):434-438. |
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| 作者姓名： | 陈恺 戴敏 张志胜 陈平 史金飞 |
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| 作者单位： | 1. 东南大学机械工程学院,南京,211189
2. 淮海工学院,连云港,222005 |
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| 基金项目： | The National Natural Science Foundation of China No．50805023 the Science and Technology Support Program of Jiang-su Province No．BE2008081 the Transformation Program of Science and Technology Achievements of Jiangsu Province No．BA2010093 the Program for Special Talent in Six Fields of Jiangsu Province No．2008144． |
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| 摘 要： | 为了分割QFN表面的缺陷,提出一种基于反向萤火虫算法的大津多阈值分割法.首先,将大津阈值分割扩展为大津多阈值分割.其次,提出了一种基于反向学习的萤火虫算法.在该算法中,生成的反向萤火虫用于增加萤火虫的多样性和全局搜索能力.然后,将基于反向学习的萤火虫算法应用于多阈值分割.最后,使用所提出的方法对QFN缺陷图像进行阈值分割实验,并将结果与穷举法、基于粒子群算法的大津多阈值分割法、基于萤火虫算法的大津多阈值分割法进行比较.实验结果表明,所提方法能更有效地分割QFN表面缺陷,且分割速度快.
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| 关 键 词： | QFN表面缺陷 反向学习 萤火虫算法 大津多阈值算法 |
Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning |
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| Chen Kai,Dai Min,Zhang Zhisheng,Chen Ping,Shi Jinfei.Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning[J].Journal of Southeast University(English Edition),2014(4):434-438. |
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| Authors: | Chen Kai Dai Min Zhang Zhisheng Chen Ping Shi Jinfei |
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| Institution: | Chen Kai, Dai Min, Zhang Zhisheng, Chen Ping, Shi Jinfei ( 1. Mechanical Engineering School, Southeast University, Nanjing 211189, China) (2 Huaihal Institute of Technology, Lianyungang 222005, China) |
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| Abstract: | To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods. |
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| Keywords: | quad flat non-lead QFN surface defects opposition-learning firefly algorithm multilevel Otsu thresholding algorithm |
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