Translator Disclaimer
15 May 2012 Artificial neural network does better spatiotemporal compressive sampling
Author Affiliations +
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
5 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Fractal properties from 2D curvature on multiple scales
Proceedings of SPIE (June 22 1993)
Neural Network For Optical Flow Estimation
Proceedings of SPIE (February 28 1990)
Mathematical theories of shape: do they model perception?
Proceedings of SPIE (August 31 1991)
Neural networks for vision-based collision avoidance
Proceedings of SPIE (March 01 1994)
A novel contour detection method
Proceedings of SPIE (November 29 2012)

Back to Top