[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]

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

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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

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

Keywords:

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

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.

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]

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