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[null,"1.","Record Nr.",null,"UNINA9910811684503321"]
[null,null,"Autore",null,"Zhdanov Mikhail Semenovich"]
[null,null,"Titolo",null,"Inverse theory and applications in geophysics / / Michael S. Zhdanov"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam, Netherlands : , : Elsevier, , 2015"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"0-444-62712-X"]
[null,null,"Edizione",null,"[Second edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (731 p.)"]
[null,null,"Collana",null,"Methods in Geochemistry and Geophysics ; ; v.36"]
[null,null,"Disciplina",null,"550.1515"]
[null,null,"Soggetti",null,"Inversion (Geophysics)"]
[{"$nulls":4},"Geophysics - Measurement"]
[{"$nulls":4},"Functional analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front Cover; Inverse Theory and Applications in Geophysics; Copyright; Dedication; Contents; Preface to the Second Edition; Preface; Part I: Introduction to Inversion Theory; Chapter 1: Forward and Inverse Problems in Science and Engineering; 1.1 Formulation of Forward and Inverse Problems for Different Physical Fields; 1.1.1 Gravity Field; 1.1.2 Magnetic Field; 1.1.3 Electromagnetic Field; 1.1.4 Seismic Wavefield; 1.2 Existence and Uniqueness of the Inverse Problem Solutions; 1.2.1 Existence of the Solution; 1.2.2 Uniqueness of the Solution; 1.2.3 Practical Uniqueness"]
[{"$nulls":4},"1.3 Instability of the Inverse Problem Solution References; Chapter 2: Ill-Posed Problems and the Methods of Their Solution; 2.1 Sensitivity and Resolution of Geophysical Methods; 2.1.1 Formulation of the Inverse Problem in General Mathematical Spaces; 2.1.2 Sensitivity; 2.1.3 Resolution; 2.2 Formulation of Well-Posed and Ill-Posed Problems; 2.2.1 Well-Posed Problems; 2.2.2 Conditionally Well-Posed Problems; 2.2.3 Quasi-Solution of the Ill-Posed Problem; 2.3 Foundations of Regularization Methods of Inverse Problem Solution; 2.3.1 Regularizing Operators; 2.3.2 Stabilizing Functionals"]
[{"$nulls":4},"2.3.3 Tikhonov Parametric Functional2.4 Family of Stabilizing Functionals; 2.4.1 Stabilizing Functionals Revisited; 2.4.2 Representation of a Stabilizing Functional in the Form of a Pseudo-Quadratic Functional; 2.5 Definition of the Regularization Parameter; 2.5.1 Optimal Regularization Parameter Selection; 2.5.2 L-Curve Method of Regularization Parameter Selection;  References; Part II: "]
[{"$nulls":4},"Methods of the Solution of Inverse Problems; Chapter 3: Linear Discrete Inverse Problems; 3.1 Linear Least-Squares Inversion; 3.1.1 The Linear Discrete Inverse Problem"]
[{"$nulls":4},"3.1.2 Systems of Linear Equations and Their General SolutionsMinimization of the misfit functional; 3.1.3 The Data Resolution Matrix; 3.2 Solution of the Purely Underdetermined Problem; 3.2.1 Underdetermined System of Linear Equations; 3.2.2 The Model Resolution Matrix; 3.3 Weighted Least-Squares Method; 3.4 Applying the Principles of Probability Theory to a Linear Inverse Problem; 3.4.1 Some Formulae and Notations from Probability Theory; 3.4.2 Maximum Likelihood Method; 3.4.3 Chi-Square Fitting; 3.5 Regularization Methods; 3.5.1 The Tikhonov Regularization Method"]
[{"$nulls":4},"3.5.2 Application of SLDM Method in Regularized Linear Inverse Problem Solution3.5.3 Integrated Sensitivity; 3.5.4 Definition of the Weighting Matrices for the Model Parameters and Data; 3.5.5 Controlled Sensitivity; 3.5.6 Approximate Regularized Solution of the Linear Inverse Problem; 3.5.7 The Levenberg-Marquardt Method; 3.5.8 The Maximum a Posteriori Estimation Method (the Bayes Estimation); 3.6 The Backus-Gilbert Method; 3.6.1 The Data Resolution Function; 3.6.2 The Spread Function; 3.6.3 Regularized Solution in the Backus-Gilbert Method;  References"]
[{"$nulls":4},"Chapter 4: Iterative Solutions of the Linear Inverse Problem"]
[null,null,null,"Sommario/riassunto","Geophysical Inverse Theory and Applications, Second Edition, brings together fundamental results developed by the Russian mathematical school in regularization theory and combines them with the related research in geophysical inversion carried out in the West. It presents a detailed exposition of the methods of regularized solution of inverse problems based on the ideas of Tikhonov regularization, and shows the different forms of their applications in both linear and nonlinear methods of geophysical inversion. It's the first book of its kind to treat many kinds of inversion and imaging techni"]
[null,"2.","Record Nr.",null,"UNINA9910484687903321"]
[null,null,"Titolo",null,"Computer Vision – ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part II / / edited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-69532-8"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (733 pages)"]
[null,null,"Collana",null,"Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 12623"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Visió per ordinador"]
[{"$nulls":4},"Reconeixement de formes (Informàtica)"]
[{"$nulls":4},"Congressos"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Low-Level Vision, Image Processing -- Image Inpainting with Onion Convolutions -- Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network -- Second-order Camera-aware Color Transformation for Cross-domain Person Re-identification -- CS-MCNet:A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation -- MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining -- Degradation Model Learning for Real-World Single Image Super-resolution -- Chromatic Aberration Correction Using Cross-Channel "]
[{"$nulls":4},"Prior in Shearlet Domain -- Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment -- Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallax -- Low-light Color Imaging via Dual Camera Acquisition -- Frequency Attention Network: Blind Noise Removal for Real Images -- Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network -- Color Enhancement usingGlobal Parameters and Local Features Learning -- An Efficient Group Feature Fusion Residual Network for Image Super-Resolution -- Adversarial Image Composition with Auxiliary Illumination -- Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network -- Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning -- Multi-scale Attentive Residual Dense Network for Single Image Rain Removal -- FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization -- Human Motion Deblurring using Localized Body Prior -- Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection -- Deep Snapshot HDR Imaging Using Multi-Exposure Color Filter Array -- Deep Priors inside an Unrolled and Adaptive Deconvolution Model -- Motion and Tracking -- Adaptive Spatio-Temporal Regularized Correlation Filters for UAV-based Tracking -- Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation -- Self-supervised Sparse toDense Motion Segmentation -- Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras -- Adversarial Refinement Network for Human Motion Prediction -- Semantic Synthesis of Pedestrian Locomotion -- Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation -- Visual Tracking by TridentAlign and Context Embedding -- Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking -- A Two-Stage Minimum Cost Multicut Approach to Self-Supervised Multiple Person Tracking -- Learning Local Feature Descriptors for Multiple Object Tracking -- VAN: Versatile Affinity Network for End-to-end Online Multi-Object Tracking -- COMET: Context-Aware IoU-Guided Network for Small Object Tracking -- Adversarial Semi-Supervised Multi-Domain Tracking -- Tracking-by-Trackers with a Distilled and Reinforced Model -- Motion Prediction Using Temporal Inception Module -- A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking -- Modeling Cross-Modal interaction in a Multi-detector, Multi-modal Tracking Framework -- Dense Pixel-wise Micro-motion Estimation of Object Surface by using Low Dimensional Embedding of Laser Speckle Pattern."]
[null,null,null,"Sommario/riassunto","The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually."]