Sensor fusion using K-nearest neighbor concepts

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

doi:10.1117/12.25272

1 April 1991 Sensor fusion using K-nearest neighbor concepts

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

Proceedings Volume 1383, Sensor Fusion III: 3D Perception and Recognition; (1991) https://doi.org/10.1117/12.25272
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

A new K-nearest neighbor (KNN) statistic is introduced 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.

Citation Download Citation

David R. Scott, Gerald M. Flachs, and Patrick T. Gaughan "Sensor fusion using K-nearest neighbor concepts", Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); https://doi.org/10.1117/12.25272

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available