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10 May 2012 Compressive sampling approach to visual attention in image scene analysis
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Many image scene analysis applications require a computational approach to visual attention. The foreground in these applications is typically sparse in spatial support. Compressive sampling enables an approach to reconstruct the sparse map of image regions that stand out from the background using fewer measurements. A convex optimization algorithm, for instance, can be used to recover the sparse map in the wavelet domain. Besides being sparse in the transform domain, the background of natural images has an interesting property that the amplitude of the averaged Fourier spectrum is approximately proportional to the inverse of the frequency. This further enables us to approximate an average background signal for extracting the out-of-ordinary foreground signal corresponding to objects of interest.
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Anurag Singh, Michael A. Pratt, and Chee-Hung Henry Chu "Compressive sampling approach to visual attention in image scene analysis", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010M (10 May 2012);

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