Physics of automatic target recognition

Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, senso...

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Détails bibliographiques
Autres auteurs: Sadjadi, Firooz A, 19..- (Directeur de la publication), Javidi, Bahram, physicien (Directeur de la publication)
Format: Livre numérique
Langue:Anglais
Publié: New York, NY : Springer New York : Springer e-books [20..].
Cham : Springer Nature
Collection:Advanced Sciences and Technologies for Security Applications 3
Accès en ligne:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Note: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Physics of Automatic Target Recognition, Texte imprimé, 9780387367422
Table des matières:
  • 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