Threshold segmentation algorithm for infrared small target in agriculture and forestry fire
A novel image segmentation for infrared small target of agriculture and forestry fire is proposed in this paper. Usually, Maximum Variance Image Segmentation method (Otsu) is a popular non-parametric method in image segmentation. However, it needs a lot computation and has poor real-time quality. Th...
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| Vydáno v: | Chinese Control Conference s. 4020 - 4025 |
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| Jazyk: | angličtina |
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TCCT
01.07.2016
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| ISSN: | 1934-1768 |
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| Abstract | A novel image segmentation for infrared small target of agriculture and forestry fire is proposed in this paper. Usually, Maximum Variance Image Segmentation method (Otsu) is a popular non-parametric method in image segmentation. However, it needs a lot computation and has poor real-time quality. Thus it is hard to be wide applied in many situations. To over come this issue, a constructive approach to obtain optimal threshold of between-class variance as fitness function for Otsu by particle swarm optimization (PSO), reduce the amount of computation and improve real-time performance. The performance of the proposed method is evaluated through infrared small target of agriculture and forestry fire. The experimental results demonstrate the effectiveness of the proposed method. |
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| AbstractList | A novel image segmentation for infrared small target of agriculture and forestry fire is proposed in this paper. Usually, Maximum Variance Image Segmentation method (Otsu) is a popular non-parametric method in image segmentation. However, it needs a lot computation and has poor real-time quality. Thus it is hard to be wide applied in many situations. To over come this issue, a constructive approach to obtain optimal threshold of between-class variance as fitness function for Otsu by particle swarm optimization (PSO), reduce the amount of computation and improve real-time performance. The performance of the proposed method is evaluated through infrared small target of agriculture and forestry fire. The experimental results demonstrate the effectiveness of the proposed method. |
| Author | Wang, Yuchao Lin, Dehua Fu, Huixuan |
| Author_xml | – sequence: 1 givenname: Yuchao surname: Wang fullname: Wang, Yuchao email: wangyuchao@hrbeu.edu.cn organization: College of Automation, Harbin Engineering University, 150001, China – sequence: 2 givenname: Huixuan surname: Fu fullname: Fu, Huixuan organization: College of Automation, Harbin Engineering University, 150001, China – sequence: 3 givenname: Dehua surname: Lin fullname: Lin, Dehua email: lin_dehua@hrbeu.edu.cn organization: Harbin Marine Boiler and Turbine Research Institute, 150001, China |
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| Snippet | A novel image segmentation for infrared small target of agriculture and forestry fire is proposed in this paper. Usually, Maximum Variance Image Segmentation... |
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| SubjectTerms | Agriculture Clustering algorithms Computation Fire recognition Fires Forestry Image segmentation Infrared image Otsu Particle swarm optimization Signal processing algorithms Swarm intelligence Thresholds Variance |
| Title | Threshold segmentation algorithm for infrared small target in agriculture and forestry fire |
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