Compressed sensing holds the promise for radically novel sensors that can perfectly reconstruct images using considerably less samples of data than required by the otherwise general Shannon sampling theorem. In surveillance systems however, it is also desirable to cue regions of the image where objects of interest may exist. Thus in this paper, we are interested in imaging interesting objects in a scene, without necessarily seeking perfect reconstruction of the whole image. We show that our goals are achieved by minimizing a modified L2-norm criterion with good results when the reconstruction of only specific objects is of interest. The method yields a simple closed form analytical solution that does not require iterative processing. Objects can be meaningfully sensed in considerable detail while heavily compressing the scene elsewhere. Essentially, this embeds the object detection and clutter discrimination function in the sensing and imaging process.

Date Published: 8 October 2007
PDF: 10 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66960S (8 October 2007); doi: 10.1117/12.740080

Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)