Artificial neural network does better spatiotemporal compressive sampling

Soo-Young Lee; Charles Hsu; Harold Szu

doi:10.1117/12.923619

15 May 2012 Artificial neural network does better spatiotemporal compressive sampling

Soo-Young Lee, Charles Hsu, Harold Szu

Author Affiliations +

Proceedings Volume 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X; 84010G (2012) https://doi.org/10.1117/12.923619
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Soo-Young Lee, Charles Hsu, and Harold Szu "Artificial neural network does better spatiotemporal compressive sampling", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010G (15 May 2012); https://doi.org/10.1117/12.923619

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