|Table of Contents|

[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:

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[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.
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[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.
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[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]
Last Update: 2014-12-20