Evolving network architectures with custom computers for multi-spectral feature identification

R. Porter, M. Gokhale, N. Harvey, S. Perkins, C. Young

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper investigates the design of evolvable FPGA circuits where the design space is severely constrained to an interconnected network of meaningful high-level operators. The specific design domain is image processing, especially pattern recognition in remotely sensed images. Preliminary experiments are reported that compare neural networks with a recently introduced variant known as morphological networks. A novel network node is then presented that is particularly suited to the problem of pattern recognition in multi-spectral data sets. More specifically, the node can exploit both spectral and spatial information, and implements both feature extraction and classification components of a typical image processing pipeline. Once trained, the network can be applied to large image data sets, for at the sensor to extract features of interest with two orders of magnitude speed-up compared to software implementations.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd NASA/DoD Workshop on Evolvable Hardware, EH 2001
EditorsRicardo Salem Zebulum, Jason Lohn, Adrian Stoica, Didier Keymeulen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-270
Number of pages10
ISBN (Electronic)0769511805
DOIs
StatePublished - 2001
Externally publishedYes
Event3rd NASA/DoD Workshop on Evolvable Hardware, EH 2001 - Long Beach, United States
Duration: Jul 12 2001Jul 14 2001

Publication series

NameProceedings - NASA/DoD Conference on Evolvable Hardware, EH
Volume2001-January
ISSN (Print)1550-6029

Other

Other3rd NASA/DoD Workshop on Evolvable Hardware, EH 2001
Country/TerritoryUnited States
CityLong Beach
Period7/12/017/14/01

ASJC Scopus subject areas

  • General Engineering

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