Sensor fusion using K-nearest neighbor concepts

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

Sensor fusion using K-nearest neighbor concepts

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

Informatics, Computing, and Cyber Systems, School of

Research output: Contribution to journal › Conference article › peer-review

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 language	English (US)
Pages (from-to)	367-378
Number of pages	12
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	1383
State	Published - 1991
Event	Sensor Fusion III: 3-D Perception and Recognition - Boston, MA, USA Duration: Nov 5 1990 → Nov 8 1990

ASJC Scopus subject areas

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

Cite this

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title = "Sensor fusion using K-nearest neighbor concepts",

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.",

author = "Scott, {David R.} and Flachs, {Gerald M.} and Gaughan, {Patrick T.}",

year = "1991",

language = "English (US)",

volume = "1383",

pages = "367--378",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Sensor Fusion III: 3-D Perception and Recognition ; Conference date: 05-11-1990 Through 08-11-1990",

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TY - JOUR

T1 - Sensor fusion using K-nearest neighbor concepts

AU - Scott, David R.

AU - Flachs, Gerald M.

AU - Gaughan, Patrick T.

PY - 1991

Y1 - 1991

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0025839041&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025839041&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0025839041

SN - 0277-786X

VL - 1383

SP - 367

EP - 378

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Sensor Fusion III: 3-D Perception and Recognition

Y2 - 5 November 1990 through 8 November 1990

ER -

Sensor fusion using K-nearest neighbor concepts

Abstract

ASJC Scopus subject areas

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