[1] Chen Kai, Dai Min, Zhang Zhisheng, Chen Ping, et al. Defect image segmentation using multilevel thresholdingbased on firefly algorithm with opposition-learning [J]. Journal of Southeast University (English Edition), 2014, 30 (4): 434-438. [doi:10.3969/j.issn.1003-7985.2014.04.006]

Copy

Defect image segmentation using multilevel thresholdingbased on firefly algorithm with opposition-learning()

基于反向萤火虫算法的多阈值缺陷图像分割

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 4

Page:

434-438

Research Field:

Computer Science and Engineering

Publishing date:

2014-12-31

Info

Title:

Defect image segmentation using multilevel thresholdingbased on firefly algorithm with opposition-learning

基于反向萤火虫算法的多阈值缺陷图像分割

Author(s):

Chen Kai¹;  Dai Min¹;  Zhang Zhisheng¹;  Chen Ping¹;  Shi Jinfei²

¹Mechanical Engineering School, Southeast University, Nanjing 211189, China
²Huaihai Institute of Technology, Lianyungang 222005, China

陈恺¹;  戴敏¹;  张志胜¹;  陈平¹;  史金飞²

¹东南大学机械工程学院, 南京211189; ²淮海工学院, 连云港222005

Keywords:

quad flat non-lead(QFN)surface defects;  opposition-learning;  firefly algorithm;  multilevel Otsu thresholding algorithm

QFN表面缺陷;  反向学习;  萤火虫算法;  大津多阈值算法

PACS:

TP391.41

DOI:

10.3969/j.issn.1003-7985.2014.04.006

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.

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.

References:

[1] Otsu N. Threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.
[2] Yin P Y. A fast scheme for optimal thresholding using genetic algorithms [J]. Signal Processing, 1999, 72(2): 85-95.
[3] Ghamisi P, Couceiro M S, Benediktsson J A, et al. An efficient method for segmentation of images based on fractional calculus and natural selection [J]. Experts Systems with Applications, 2012, 39(16): 12407-12417.
[4] Gao K L, Dong M, Zhu L Q, et al. Image segmentation method based upon Otsu ACO Algorithm [C]//Information and Automation: Communications in Computer and Information Science. Springer, 2011, 86: 574-580.
[5] Sathya P D, Kayalvizhi R. Optimal multilevel thresholding using bacterial foraging algorithm [J]. Expert Systems with Applications, 2011, 38(12): 15549-15564.
[6] Yang X S. Firefly algorithms for multimodal optimization [C]//Stochastic Algorithms: Foundations and Applications. Springer, 2009: 169-178.
[7] Horng M H, Liou R J. Multilevel minimum cross entropy threshold selection based on the firefly algorithm [J]. Expert Systems with Applications, 2011, 38(12): 14805-14811.
[8] Hassanzadeh T, Vojodi H, Eftekhari A M. An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm [C]//2011 Seventh International Conference on Natural Computation. Shanghai, China, 2011: 1817-1821.
[9] Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence [C]//International Conference on Intelligent Agents, Web Technologies and Internet Commerce. Canberra, Australia, 2005, 1: 695-701.

Memo

Memo:

Biographies: Chen Kai(1986—), female, graduate; Zhang Zhisheng(corresponding author), male, doctor, professor, oldbc@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.50805023), the Science and Technology Support Program of Jiangsu 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).
Citation: Chen Kai, Dai Min, Zhang Zhisheng, et al. Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning.[J].Journal of Southeast University(English Edition), 2014, 30(4):434-438.[doi:10.3969/j.issn.1003-7985.2014.04.006]

Common functions