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Proceedings Paper

Artificial neural network does better spatiotemporal compressive sampling
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Paper Abstract

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Paper Details

Date Published: 15 May 2012
PDF: 5 pages
Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010G (15 May 2012); doi: 10.1117/12.923619
Show Author Affiliations
Soo-Young Lee, KAIST (Korea, Republic of)
Charles Hsu, The Catholic Univ. of America (United States)
Harold Szu, The Catholic Univ. of America (United States)


Published in SPIE Proceedings Vol. 8401:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
Harold Szu, Editor(s)

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