Probabilistic DBNN with applications to sensor fusion and object recognition

Shang Hung Lin, S. Y. Kung, Long Ji Lin

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

A probabilistic variant of the decision-based neural network (DBNN) that is meant to better estimate probability density functions corresponding to different pattern classes is developed. New learning rules for the probabilistic DBNN are derived. Experimental results demonstrate noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. A multiple sensor fusion system for object classification is developed using probabilistic output values of the DBNN.

Original languageEnglish (US)
Pages333-342
Number of pages10
StatePublished - Jan 1 1995
EventProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
Duration: Aug 31 1995Sep 2 1995

Other

OtherProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
CityCambridge, MA, USA
Period8/31/959/2/95

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Lin, S. H., Kung, S. Y., & Lin, L. J. (1995). Probabilistic DBNN with applications to sensor fusion and object recognition. 333-342. Paper presented at Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95), Cambridge, MA, USA, .