Title :
Neural networks for signal/image processing using the Princeton Engine multi-processor
Author :
Binenbaum, N. ; Dias, L. ; Hsieh, P. ; Ju, C.H. ; Markel, S. ; Pearson, J.C. ; Taylor, H., Jr.
Author_Institution :
David Sarnoff Res. Center, Princeton, NJ, USA
fDate :
30 Sep-1 Oct 1991
Abstract :
The authors describe a modular neural network system for the removal of impulse noise from the composite video signal of television receivers, and the use of the Princeton Engine multi-processor for real-time performance assessment. This system out-performs alternative methods, such as median filters and matched filters. The system uses only eight neurons, and can be economically implemented in VLSI
Keywords :
image processing; learning (artificial intelligence); neural chips; signal processing; television receivers; video signals; Princeton Engine multi-processor; VLSI; composite video signal; image processing; impulse noise removal; modular neural network; real-time performance assessment; signal processing; television receivers; training; Computer aided manufacturing; Computer networks; Engines; Image processing; Matched filters; Neural networks; Real time systems; Signal processing; TV receivers; Very large scale integration;
Conference_Titel :
Neural Networks for Signal Processing [1991]., Proceedings of the 1991 IEEE Workshop
Conference_Location :
Princeton, NJ
Print_ISBN :
0-7803-0118-8
DOI :
10.1109/NNSP.1991.239481
