A well-ordered feature space mapping for sensor fusion

Gerald M. Flachs, Cynthia L. Beer, David R. Scott

Research output: Contribution to journalConference article

Abstract

An approach is presented for mapping a multisensor feature space into a space that is well-ordered for vision tasks. A new statistic, the tie statistic (TS), is introduced for measuring the difference between two probability density functions (pdfs). The TS is related to the Kolmogorov-Smirnov statistic (KS) to demonstrate its ability to decide whether or not a sample came from a known pdf. The TS is used to map the measured feature space into a simplified decision space. In the mapping process, the tie statistic is itself a random variable that has a distribution that can be parametrically approximated by the Beta distribution. The tie mapping process is presented and applied to solve two important vision problems.

Original languageEnglish (US)
Pages (from-to)152-161
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1100
DOIs
StatePublished - Sep 14 1989
Externally publishedYes
EventSensor Fusion II 1989 - Orlando, United States
Duration: Mar 27 1989Mar 31 1989

Fingerprint

Ordered Space
Sensor Fusion
multisensor fusion
Tie
Feature Space
Statistic
Fusion reactions
Statistics
statistics
Sensors
Kolmogorov-Smirnov Statistic
Beta distribution
random variables
Probability density function
probability density functions
Random variables
Random variable
Demonstrate

ASJC Scopus subject areas

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

Cite this

A well-ordered feature space mapping for sensor fusion. / Flachs, Gerald M.; Beer, Cynthia L.; Scott, David R.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 1100, 14.09.1989, p. 152-161.

Research output: Contribution to journalConference article

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