Cited By
View all- Zhang YZheng LZhang Y(2019)Small Infrared Target Detection via a Mexican-Hat DistributionApplied Sciences10.3390/app92455709:24(5570)Online publication date: 17-Dec-2019
Infrared small target detection based on salient region extraction and gradient vector processing
Pages 422 - 426
Abstract
Infrared small target detection plays a significant role in modern infrared search and track systems(IRST) and has also received much attention. Based on this background, a novel approach to detect infrared small target based on salient region extraction (SRE) and gradient vector processing (GVP) is proposed in this paper. This approach consists of three parts: pre-processing based on fast median filtering, SRE based on spectral residual algorithm (SRA) processing and similarity analysis algorithm (SAA) processing, and target detection in infrared gradient vector field (IGVF). Both theory and experimental have shown that the proposed algorithm can detect the target more robustly and accurately, and spend less time processing compared with the existing infrared small target detection algorithm. It is a small infrared target detection method with superior performance and good application prospect.
References
[1]
Chen Y, Song B, Du X, et al (2019). Infrared Small Target Detection through Multiple Feature Analysis Based on Visual Saliency [J]. IEEE Access, PP(99), 1--1.
[2]
Deng H, Sun X, Liu M. et al (2017). Entropy-based window selection for detecting dim and small infrared targets [J]. Pattern Recognition, 61, 66--77.
[3]
Zhou, Quan, Deng, Lizhen, Li, Yansheng, et al (2018). Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection [J]. Multimedia tools and applications, 77(9), 10539--10551.
[4]
Wu T, He W Z, Chen X L, et al (2016). Detection algorithm of single frame infrared small target based on local features [J]. Laser & Infrared, 46(3), 368--371.
[5]
Zhou, Quan, Deng, Lizhen, Li, Yansheng, et al (2018). Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection [J]. Multimedia tools and applications, 77(9), 10539--10551.
[6]
Han J, Yong M, Bo Z, et al (2014). A Robust Infrared Small Target Detection Algorithm Based on Human Visual System [J]. IEEE Geoscience & Remote Sensing Letters, 11(12), 2168--2172.
[7]
Liu D, Cao L, Li Z, et al (2018). Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7), 1--27.
[8]
Bi Y, Bai X, Jin T, et al (2017). Multiple Feature Analysis for Infrared Small Target Detection [J]. IEEE Geoscience and Remote Sensing Letters, 14(8), 1333--1337.
Index Terms
- Infrared small target detection based on salient region extraction and gradient vector processing
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Published In
September 2019
803 pages
ISBN:9781450372985
DOI:10.1145/3366194
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Association for Computing Machinery
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Published: 20 September 2019
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RICAI 2019
RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
September 20 - 22, 2019
Shanghai, China
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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
Overall Acceptance Rate 140 of 294 submissions, 48%
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View all- Zhang YZheng LZhang Y(2019)Small Infrared Target Detection via a Mexican-Hat DistributionApplied Sciences10.3390/app92455709:24(5570)Online publication date: 17-Dec-2019
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