Sensor fusion using K-nearest neighbor concepts

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

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

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)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsPaul S. Schenker
PublisherPubl by Int Soc for Optical Engineering
Pages367-378
Number of pages12
Volume1383
StatePublished - 1991
EventSensor Fusion III: 3-D Perception and Recognition - Boston, MA, USA
Duration: Nov 5 1990Nov 8 1990

Other

OtherSensor Fusion III: 3-D Perception and Recognition
CityBoston, MA, USA
Period11/5/9011/8/90

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Scott, D. R., Flachs, G. M., & Gaughan, P. T. (1991). Sensor fusion using K-nearest neighbor concepts. In P. S. Schenker (Ed.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 1383, pp. 367-378). Publ by Int Soc for Optical Engineering.