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Infrared small target detection based on target-background separation via local MCA sparse representation
Author(s): Hao Fu; Yunli Long; Jungang Yang; Wei An
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Paper 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.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200X (21 July 2017); doi: 10.1117/12.2281782
Show Author Affiliations
Hao Fu, National Univ. of Defense Technology (China)
Yunli Long, National Univ. of Defense Technology (China)
Jungang Yang, National Univ. of Defense Technology (China)
Wei An, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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