Share Email Print

Proceedings Paper

Compressive sampling by artificial neural networks for video
Author(s): Harold Szu; Charles Hsu; Jeffrey Jenkins; Kitt Reinhardt
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We describe a smart surveillance strategy for handling novelty changes. Current sensors seem to keep all, redundant or not. The Human Visual System's Hubel-Wiesel (wavelet) edge detection mechanism pays attention to changes in movement, which naturally produce organized sparseness because a stagnant edge is not reported to the brain's visual cortex by retinal neurons. Sparseness is defined as an ordered set of ones (movement or not) relative to zeros that could be pseudo-orthogonal among themselves; then suited for fault tolerant storage and retrieval by means of Associative Memory (AM). The firing is sparse at the change locations. Unlike purely random sparse masks adopted in medical Compressive Sensing, these organized ones have an additional benefit of using the image changes to make retrievable graphical indexes. We coined this organized sparseness as Compressive Sampling; sensing but skipping over redundancy without altering the original image. Thus, we turn illustrate with video the survival tactics which animals that roam the Earth use daily. They acquire nothing but the space-time changes that are important to satisfy specific prey-predator relationships. We have noticed a similarity between the mathematical Compressive Sensing and this biological mechanism used for survival. We have designed a hardware implementation of the Human Visual System's Compressive Sampling scheme. To speed up further, our mixedsignal circuit design of frame differencing is built in on-chip processing hardware. A CMOS trans-conductance amplifier is designed here to generate a linear current output using a pair of differential input voltages from 2 photon detectors for change detection---one for the previous value and the other the subsequent value, ("write" synaptic weight by Hebbian outer products; "read" by inner product & pt. NL threshold) to localize and track the threat targets.

Paper Details

Date Published: 8 June 2011
PDF: 17 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805803 (8 June 2011); doi: 10.1117/12.887596
Show Author Affiliations
Harold Szu, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Charles Hsu, Trident Systems Inc. (United States)
Jeffrey Jenkins, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Kitt Reinhardt, Air Force Office of Scientific Research (United States)

Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?