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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 50, Iss. 11 — Apr. 10, 2011
  • pp: 1601–1605

Sparse-representation-based clutter metric

Cui Yang, Jie Wu, Qian Li, and Jian-Qi Zhang  »View Author Affiliations

Applied Optics, Vol. 50, Issue 11, pp. 1601-1605 (2011)

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Background clutter is becoming one of the most important factors affecting the target acquisition performance of electro-optical imaging systems. A novel clutter metric based on sparse representation is proposed in this paper. Based on sparse representation, the similarity vector is defined to describe the similarity between the background and the target in the feature domain, which is a typical feature of the background clutter. This newly proposed metric is applied to the Search_2 data set, and the experiment results show that its prediction correlates well with the detection probability of observers.

© 2011 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.2960) Image processing : Image analysis
(110.2970) Imaging systems : Image detection systems
(100.4995) Image processing : Pattern recognition, metrics

ToC Category:
Image Processing

Original Manuscript: August 25, 2010
Revised Manuscript: January 21, 2011
Manuscript Accepted: February 1, 2011
Published: April 7, 2011

Cui Yang, Jie Wu, Qian Li, and Jian-Qi Zhang, "Sparse-representation-based clutter metric," Appl. Opt. 50, 1601-1605 (2011)

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