Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast i...

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Vydáno v:Journal of Electrical and Computer Engineering Ročník 2017; číslo 2017; s. 1 - 12-005
Hlavní autoři: Wang, Wuli, Wang, Yong, Duan, Liming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Limiteds 01.01.2017
Hindawi Publishing Corporation
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Analysis
Artificial intelligence
Cybernetics
Engineering
Filtration
Fitness
Genetic algorithms
Image enhancement
Image processing
Image segmentation
Markov analysis
Methods
Neighborhoods
Neural networks
Nondestructive testing
Run time (computers)
Signal processing
Target recognition
Tomography
China
ISSN:2090-0147, 2090-0155
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Shrnutí:Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2090-0147
2090-0155
DOI:10.1155/2017/1735176