Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Aided and Automatic Target Recognition Based Upon Sensory Inputs From Image Forming Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Bounds on Shape Recognition Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Guest Editorial Introduction To The Special Issue On Automatic Target Detection And Recognition
IEEE Transactions on Image Processing
Automatic target recognition organized via jump-diffusion algorithms
IEEE Transactions on Image Processing
Statistical Estimation and Classification on Commutative Covariance Structures
Automation and Remote Control
Application-specific compression for time delay estimation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
On signal representations within the Bayes decision framework
Pattern Recognition
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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.