Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization

A method based on particle swarm optimization (PSO) for steel strip image segmentation was presented. Considered the traditional markov method is hard to get good effect in global optimization solution, the particle swarm optimization is used to enhance search capacity in the multi-dimensional space...

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Main Authors: Z.Y. Zhang, X.Y. He, X.H. Sun, J.H. Wang, F.S. Wang
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2015-12-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/4204
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author Z.Y. Zhang
X.Y. He
X.H. Sun
J.H. Wang
F.S. Wang
author_facet Z.Y. Zhang
X.Y. He
X.H. Sun
J.H. Wang
F.S. Wang
author_sort Z.Y. Zhang
collection DOAJ
description A method based on particle swarm optimization (PSO) for steel strip image segmentation was presented. Considered the traditional markov method is hard to get good effect in global optimization solution, the particle swarm optimization is used to enhance search capacity in the multi-dimensional space and determine the parameters of markov random field to optimize the objective function which comes from the random field. The method is compared with the classical simulated annealing algorithm. The segmentation effect is quantitative assessed by pixel dispersion, coincidence degree and area of detesting. Results show that the proposed algorithm performs better than the traditional algorithm in the three aspects. It can rapidly get the better segmentation result with satisfactory noise rejection and edge preserving. The robustness to noise and the smoothness are remarkably improved.
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spelling doaj.art-02ced2aa80074667b06b013721c07f552022-12-21T19:03:48ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162015-12-014610.3303/CET1546035Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm OptimizationZ.Y. ZhangX.Y. HeX.H. SunJ.H. WangF.S. WangA method based on particle swarm optimization (PSO) for steel strip image segmentation was presented. Considered the traditional markov method is hard to get good effect in global optimization solution, the particle swarm optimization is used to enhance search capacity in the multi-dimensional space and determine the parameters of markov random field to optimize the objective function which comes from the random field. The method is compared with the classical simulated annealing algorithm. The segmentation effect is quantitative assessed by pixel dispersion, coincidence degree and area of detesting. Results show that the proposed algorithm performs better than the traditional algorithm in the three aspects. It can rapidly get the better segmentation result with satisfactory noise rejection and edge preserving. The robustness to noise and the smoothness are remarkably improved.https://www.cetjournal.it/index.php/cet/article/view/4204
spellingShingle Z.Y. Zhang
X.Y. He
X.H. Sun
J.H. Wang
F.S. Wang
Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
Chemical Engineering Transactions
title Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
title_full Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
title_fullStr Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
title_full_unstemmed Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
title_short Research On Steel Strip Image Segmentation Algorithm Based On Particle Swarm Optimization
title_sort research on steel strip image segmentation algorithm based on particle swarm optimization
url https://www.cetjournal.it/index.php/cet/article/view/4204
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