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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Bibliographic Details
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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Summary: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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SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7553981