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Proceedings Paper

L1-based variational methods for low-power surveillance
Author(s): Matthew S. Keegan; Kang-Yu Ni; Shankar Rao
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Paper Abstract

In this paper we introduce two novel methods for application of `1-minimization. In the first method, sparse and low-rank decomposition and compressive sensing-based retrieval are combined and applied to a low power surveillance model. The method exploits the ability of sparse and low-rank decompositions to extract significant and stationary features and the ability of compressive sensing approaches to reduce the number of measurements necessary. In the second method, a contiguity prior is added to compressive sensing methods on images and a numerical approach is proposed to solve this novel problem. Results are displayed in which the contiguity constrained method is applied to the low power surveillance model.

Paper Details

Date Published: 17 September 2013
PDF: 7 pages
Proc. SPIE 8877, Unconventional Imaging and Wavefront Sensing 2013, 88770D (17 September 2013); doi: 10.1117/12.2024253
Show Author Affiliations
Matthew S. Keegan, HRL Labs., LLC (United States)
Kang-Yu Ni, HRL Labs., LLC (United States)
Shankar Rao, HRL Labs., LLC (United States)


Published in SPIE Proceedings Vol. 8877:
Unconventional Imaging and Wavefront Sensing 2013
Jean J. Dolne; Thomas J. Karr; Victor L. Gamiz, Editor(s)

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