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Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data
September 2002 (vol. 24 no. 9)
pp. 1153-1166

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. 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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Index Terms:
Object recognition, automatic target recognition, imaging sensors, multisensor data fusion, data compression, performance metrics.
Citation:
Avinash Jain, Pierre Moulin, Michael I. Miller, Kannan Ramchandran, "Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1153-1166, Sept. 2002, doi:10.1109/TPAMI.2002.1033209
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