Information-theoretic bounds on target recognition performance

Avinash Jain; Pierre Moulin; Michael I. Miller; Kannan Ramchandran

doi:10.1117/12.395580

17 August 2000 Information-theoretic bounds on target recognition performance

Avinash Jain, Pierre Moulin, Michael I. Miller, Kannan Ramchandran

Proceedings Volume 4050, Automatic Target Recognition X; (2000) https://doi.org/10.1117/12.395580
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information- theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Applications to target recognition based on compressed sensor image data are given. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models, and optimizing system parameters.

Citation Download Citation

Avinash Jain, Pierre Moulin, Michael I. Miller, and Kannan Ramchandran "Information-theoretic bounds on target recognition performance", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); https://doi.org/10.1117/12.395580

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