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Proceedings Paper

Anomaly recovery from compressed spectral imagery via low-rank matrix minimization
Author(s): Ana Ramirez; Henry Arguello; Gonzalo R. Arce
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Paper Abstract

This work describes a methodology for the recovery of anomalies and their spectral signatures from compressively sensed multi-spectral video using Principal Component Pursuit (PCP). In video surveillance, approaches based on PCP allow the anomaly detection in a cluttered background by modeling a sequence of video frames as a large data matrix composed by a low-rank matrix plus a sparse matrix. The low-rank matrix corresponds to the stationary background and the sparse matrix captures the anomalies in the foreground. The compressive spectral video frames are attained by the use of a Coded Aperture Snapshot Spectral Imaging (CASSI) system. The CASSI system allows the compressive measurement of spectrally rich video content by simply capturing a sequence of 2D coded aperture video frames. This paper describes improved procedures for the reconstruction of the video anomalies and their spectra based on the 2-D, aperture-coded, isolated anomalies.

Paper Details

Date Published: 4 June 2011
PDF: 6 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805806 (4 June 2011); doi: 10.1117/12.887495
Show Author Affiliations
Ana Ramirez, Univ. of Delaware (United States)
Henry Arguello, Univ. of Delaware (United States)
Gonzalo R. Arce, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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