L1-based variational methods for low-power surveillance

Matthew S. Keegan; Kang-Yu Ni; Shankar Rao

doi:10.1117/12.2024253

17 September 2013 L₁-methods for low-power surveillance

Matthew S. Keegan, Kang-Yu Ni, Shankar Rao

Proceedings Volume 8877, Unconventional Imaging and Wavefront Sensing 2013; 88770D (2013) https://doi.org/10.1117/12.2024253
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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.

Citation Download Citation

Matthew S. Keegan, Kang-Yu Ni, and Shankar Rao "L₁-methods for low-power surveillance", Proc. SPIE 8877, Unconventional Imaging and Wavefront Sensing 2013, 88770D (17 September 2013); https://doi.org/10.1117/12.2024253

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