This book examines the roles of sensors, physics-based attributes, classification methods, and performance evaluation in automatic target recognition. It details target classification from small mine-like objects to large tactical vehicles. Also explored in the book are invariants of sensor and transmission transformations, which are crucial in the development of low latency and computationally manageable automatic target recognition systems.

First book on Automatic Target Recognition

Kernel-Based Nonlinear Subspace Target Detection for Hyperspectral Imagery.- Theory of Invariant Algebra and Its Use in Automatic Target Recognition.- Automatic Recognition of Underground Targets Using Time-Frequency Analysis and Optimization Techniques.- A Weighted Zak Transform, Its Properties, and Applications to Signal Processing.- Using Polarization Features of Visible Light for Automatic Landmine Detection.- The Physics of Polarization-Sensitive Optical Imaging.- Dispersion, Its Effects, and Compensation.- Multisensor Target Recognition in Image Response Space Using Evolutionary Algorithms.- Biophysics of the Eye in Computer Vision: Methods and Advanced Technologies.- Two Approaches to 3D Microorganism Recognition Using Single Exposure Online (SEOL) Digital Holography.- Distortion-Tolerant 3D Object Recognition by Using Single Exposure On-Axis Digital Holography.- Design of Distortion-Invariant Optical ID Tags for Remote Identification and Verification of Objects.- Speckle Elimination With a Maximum Likelihood Estimation and an Isoline Regularization.