Sensor fusion using K-nearest neighbor concepts

David R. Scott, Gerald M. Flachs, Patrick T. Gaughan

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

A new K-nearest neighbor (KNN) statistic is introducted to fuse information from multiple sensors/features into a single dimensional decision space for electronic vision systems. Theorems establish the relationship of the KNN statistic to other probability density function distance measures such as the Kolmogorov-Smirnov Distance and the Tie Statistic. A new KNN search algorithm is presented along with factors for selecting K. Applications include cueing and texture recognition.

Original languageEnglish (US)
Pages (from-to)367-378
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1383
StatePublished - Jan 1 1991
EventSensor Fusion III: 3-D Perception and Recognition - Boston, MA, USA
Duration: Nov 5 1990Nov 8 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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