Adaptive learning concepts and methodology for enhanced recognition system performance

Belur V. Dasarathy

doi:10.1117/12.181034

29 July 1994 Adaptive learning concepts and methodology for enhanced recognition system performance

Belur V. Dasarathy

Proceedings Volume 2234, Automatic Object Recognition IV; (1994) https://doi.org/10.1117/12.181034
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

This study presents innovative adaptive learning concepts and resulting methodology designed for enhancing the performance of recognition systems such as automatic target recognition (ATR) systems wherein robustness of performance is a significant issue. The basic underlying concept is that of learning in partially exposed environments, wherein the system is not necessarily aware of all the pattern classes that may be encountered in the operational phase. The methodology is based on such learning as required by nearest neighbor based decision systems. The paper discusses several stages of sophistication of the system design and illustrates these with two sets of numerical experiments (using the now classical iris data as well as some real-world TV image data) to bring out the subtleties of the issues involved.

Citation Download Citation

Belur V. Dasarathy "Adaptive learning concepts and methodology for enhanced recognition system performance", Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); https://doi.org/10.1117/12.181034

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