A neuronet approach to information fusion

Thomas S. Huaing, Christopher P. Hess, Hao Pan, Zhi Pei Liang

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

Abstract

Neuronet approaches offer a unique and powerful tool for nonlinear information fusion. Unlike traditional techniques, neuronets do not require explicit environmental models or descriptions of sensor characteristics. This paper describes a technique for sensor fusion which makes use of a new neural model to combine data autonomously extracted from different sources. Application of the technique to bimodal recognition of combined speech/image signals is discussed.

Original languageEnglish (US)
Title of host publication1997 IEEE 1st Workshop on Multimedia Signal Processing, MMSP 1997
EditorsYao Wang, Amy R. Reibman, B. H. Juang, Tsuhan Chen, Sun-Yuan Kung
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages45-50
Number of pages6
ISBN (Electronic)0780337808, 9780780337800
DOIs
StatePublished - Jan 1 1997
Event1st IEEE Workshop on Multimedia Signal Processing, MMSP 1997 - Princeton, United States
Duration: Jun 23 1997Jun 25 1997

Publication series

Name1997 IEEE 1st Workshop on Multimedia Signal Processing, MMSP 1997

Other

Other1st IEEE Workshop on Multimedia Signal Processing, MMSP 1997
CountryUnited States
CityPrinceton
Period6/23/976/25/97

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

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