21 July 2017 Infrared small target detection based on target-background separation via local MCA sparse representation
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104200X (2017) https://doi.org/10.1117/12.2281782
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Infrared small target detection is one of the vital techniques in infrared search and track surveillance systems. An efficient method based on target-background separation via local morphological component analysis (MCA) sparse representation is proposed in this paper. This method converts infrared small target detection problem over entire image into target-background separation over image patches according to the different morphological component between target and background. An adaptive dictionary is trained adaptively by K-singular value decomposition (K-SVD) according to infrared image, and then the dictionary is subdivided by a total-variation-like activity measure into two categories: the target component dictionary explaining target signal and background component dictionary embedding background. Finally, the interest target can be easily extracted through threshold segmentation in target component image constructed by target dictionary. The experimental results demonstrate the effectiveness of the proposed method.
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Hao Fu, Yunli Long, Jungang Yang, Wei An, "Infrared small target detection based on target-background separation via local MCA sparse representation", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200X (21 July 2017); doi: 10.1117/12.2281782; https://doi.org/10.1117/12.2281782
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