Information-Theoretic Bounds on Target Recognition
Performance Based on Degraded Image Data

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


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. 

Index Terms --- Object recognition, automatic target recognition,
imaging sensors, multisensor data fusion, data compression,
performance metrics.