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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Published in:Chinese Control Conference pp. 4020 - 4025
Main Authors: Wang, Yuchao, Fu, Huixuan, Lin, Dehua
Format: Conference Proceeding Journal Article
Language:English
Published: TCCT 01.07.2016
Subjects:
Agriculture
Clustering algorithms
Computation
Fire recognition
Fires
Forestry
Image segmentation
Infrared image
Otsu
Particle swarm optimization
Signal processing algorithms
Swarm intelligence
Thresholds
Variance
ISSN:1934-1768
Online Access:Get full text
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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.
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
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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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StartPage 4020
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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