Information-theoretic bounds on target recognition performancebased on degraded image data
Jain, A.; Moulin, P.; Miller, M.I.; Ramchandran, K.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 24, Issue 9, Sep 2002 Page(s): 1153 - 1166
Digital Object Identifier 10.1109/TPAMI.2002.1033209
Summary: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. Problems involving Gaussian, Poisson, and
multiplicative noise, and random pixel deletions are considered, as well
as least-favorable Gaussian clutter. A sixth application involving
compressed sensor image data is considered in some detail. This study
provides a systematic and computationally attractive framework for
analytically characterizing target recognition performance under
complicated, non-Gaussian models and optimizing system parameters
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