Multi-population cooperative evolution-based image segmentation algorithm for complex helical surface image

Jiande Zhang; Chenrong Huang; Ying Huo; Zhan Shi; Tinghuai Ma; Jiande Zhang; Chenrong Huang; Ying Huo; Zhan Shi; Tinghuai Ma

doi:10.3934/mbe.2020385

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 7544-7561. doi: 10.3934/mbe.2020385

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Research article Special Issues

Multi-population cooperative evolution-based image segmentation algorithm for complex helical surface image

1.
School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China
2.
Laboratory of Intelligent Manufacturing Equipment, Nanjing 211167, China
3.
School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received: 29 July 2020 Accepted: 25 October 2020 Published: 30 October 2020

Accurate image segmentation results would show a great significance to computer vision-based manufacturing for complex helical surface. However, the image segmentation for complex helical surface is always a difficult problem because of the uneven gray distribution and non-homogeneous feature patterns of its images. Therefore, a multi-direction evolutionary segmentation model is constructed and a multi-population cooperative evolution algorithm is proposed to solve the new model. According to the characteristics of gray distribution and feature patterns of complex helical surface image, an eigenvector extraction and description strategy is researched by combining gray level co-occurrence matrix algorithm with fractal algorithm, and the complex helical surface image can be described succinctly by gray feature and shape feature. Based on the description algorithm of image features, an image segmentation strategy using cooperative evolution from different eigenvector is discussed, and the helical surface image segmentation is decomposed from a single objective optimization problem to a multi-objective optimization problem to improve the accuracy of segmentation. Meanwhile, a multi-objective particle swarm optimization algorithm based on multi-directional evolution and shared archives is presented. Due to the fact that each eigenvector segmentation corresponds to one evolution direction, the collaboration of local and global segmentation can be realized by information sharing and interaction between evolution directions and the archive set. The comprehensive quality of non-dominated solution can be improved by the selection strategy of local and global optimal solution as well as the archive set maintenance. The practical numerical experiments for complex helical surface image segmentation are carried out to prove the validity of the proposed model and algorithm.
- image segmentation,
- evolutionary computation,
- helical surface,
- multi-objective optimization
Citation: Jiande Zhang, Chenrong Huang, Ying Huo, Zhan Shi, Tinghuai Ma. Multi-population cooperative evolution-based image segmentation algorithm for complex helical surface image[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7544-7561. doi: 10.3934/mbe.2020385

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Abstract

Accurate image segmentation results would show a great significance to computer vision-based manufacturing for complex helical surface. However, the image segmentation for complex helical surface is always a difficult problem because of the uneven gray distribution and non-homogeneous feature patterns of its images. Therefore, a multi-direction evolutionary segmentation model is constructed and a multi-population cooperative evolution algorithm is proposed to solve the new model. According to the characteristics of gray distribution and feature patterns of complex helical surface image, an eigenvector extraction and description strategy is researched by combining gray level co-occurrence matrix algorithm with fractal algorithm, and the complex helical surface image can be described succinctly by gray feature and shape feature. Based on the description algorithm of image features, an image segmentation strategy using cooperative evolution from different eigenvector is discussed, and the helical surface image segmentation is decomposed from a single objective optimization problem to a multi-objective optimization problem to improve the accuracy of segmentation. Meanwhile, a multi-objective particle swarm optimization algorithm based on multi-directional evolution and shared archives is presented. Due to the fact that each eigenvector segmentation corresponds to one evolution direction, the collaboration of local and global segmentation can be realized by information sharing and interaction between evolution directions and the archive set. The comprehensive quality of non-dominated solution can be improved by the selection strategy of local and global optimal solution as well as the archive set maintenance. The practical numerical experiments for complex helical surface image segmentation are carried out to prove the validity of the proposed model and algorithm.

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