Information-theoretic bounds on target recognition performance based on degraded image data
IEEE Transactions on Pattern Analysis and Machine Intelligence - Tập 24 Số 9 - Trang 1153-1166 - 2002
Tóm tắt
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.
Từ khóa
#Target recognition #Object recognition #Remote sensing #Testing #Probability #Gaussian noise #Image coding #Image sensors #Sensor phenomena and characterization #Performance analysisTài liệu tham khảo
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