Feature selection and decision space mapping for sensor fusion

Cynthia L. Beer, Gerald M. Flachs, David R. Scott, Jay B. Jordan

Research output: Contribution to journalArticlepeer-review


An information fusion approach is presented for mapping a multiple dimensional feature space into a lower dimensional decision space with simplified decision boundaries. A new statistic, called the tie statistic, is used to perform the mapping by measuring differences in probability density functions of features. These features are then evaluated based on the separation of the decision classes using a parametric beta representation for the tie statistic. The feature evaluation and fusion methods are applied to perform texture recognition.

Original languageEnglish (US)
Pages (from-to)223-234
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Mar 1 1990

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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