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Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization.

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Title: Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization.
Authors: Zhiyong SHE1 szy@xjzfu.edu.cn, Tao SONG2 songtao@guc.edu.cn, Dongpo ZHANG1 szy@xjzfu.edu.cn, Yueping FENG3 fengyp@jlu.edu.cn
Source: Technical Gazette / Tehnički Vjesnik. 2025, Vol. 32 Issue 2, p704-712. 9p.
Subjects: Rough sets, Image segmentation, Particle swarm optimization, Problem solving, Algorithms
Abstract: This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization.
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  Data: <searchLink fieldCode="AR" term="%22Zhiyong+SHE%22">Zhiyong SHE</searchLink><relatesTo>1</relatesTo><i> szy@xjzfu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tao+SONG%22">Tao SONG</searchLink><relatesTo>2</relatesTo><i> songtao@guc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Dongpo+ZHANG%22">Dongpo ZHANG</searchLink><relatesTo>1</relatesTo><i> szy@xjzfu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yueping+FENG%22">Yueping FENG</searchLink><relatesTo>3</relatesTo><i> fengyp@jlu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2025, Vol. 32 Issue 2, p704-712. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Rough+sets%22">Rough sets</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Label: Abstract
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  Data: This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.17559/TV-20240301001359
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        Text: English
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        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Problem solving
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      – SubjectFull: Algorithms
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      – TitleFull: Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization.
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            NameFull: Tao SONG
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            – D: 01
              M: 03
              Text: 2025
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              Y: 2025
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