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EXPLOR 606 $aGeochemical prospecting$vPeriodicals 608 $aPeriodicals. 615 0$aGeochemical prospecting 906 $aJOURNAL 912 $a996214886803316 996 $aJournal of geochemical exploration$985748 997 $aUNISA LEADER 07476nam 22007455 450 001 9910725100603321 005 20251225203503.0 010 $a9783031319754$b(electronic bk.) 010 $z9783031319747 024 7 $a10.1007/978-3-031-31975-4 035 $a(MiAaPQ)EBC7248824 035 $a(Au-PeEL)EBL7248824 035 $a(OCoLC)1379017738 035 $a(BIP)090181780 035 $a(PPN)270612114 035 $a(CKB)26637877500041 035 $a(DE-He213)978-3-031-31975-4 035 $a(EXLCZ)9926637877500041 100 $a20230501d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aScale Space and Variational Methods in Computer Vision $e9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21?25, 2023, Proceedings /$fedited by Luca Calatroni, Marco Donatelli, Serena Morigi, Marco Prato, Matteo Santacesaria 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (767 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14009 311 08$aPrint version: Calatroni, Luca Scale Space and Variational Methods in Computer Vision Cham : Springer International Publishing AG,c2023 9783031319747 327 $aInverse Problems in Imaging -- Explicit Diffusion of Gaussian Mixture Model Based Image Priors -- Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting -- Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators -- A Frame Decomposition of the Funk-Radon Transform -- Prony-Based Super-Resolution Phase Retrieval of Sparse, Multidimensional Signals -- Limited Electrodes Models in Electrical Impedance Tomography Reconstruction -- On Trainable Multiplicative Noise Removal Models -- Surface Reconstruction from 2D Noisy Point Cloud Data using Directional G-norm -- Regularized Material Decomposition for K-Edge Separation in Hyperspectral Computed Tomography -- Quaternary Image Decomposition with Cross-Correlation-Based Multi-Parameter Selection -- Machine and Deep Learning in Imaging -- EmNeF: Neural Fields for Embedded Variational Problems in Imaging -- GenHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials -- Compressive Learning of Deep Regularization for Denoising -- Graph Laplacian and Neural Networks for Inverse Problems in Imaging: graphLaNet -- Learning Posterior Distributions in Underdetermined Inverse Problems -- Proximal Residual Flows for Bayesian Inverse Problems -- A Model Is Worth Tens of Thousands of Examples -- Resolution-Invariant Image Classification Based on Fourier Neural Operators -- Graph Laplacian for Semi-Supervised Learning -- A Geometrically Aware Auto-Encoder for Multi-Texture Synthesis -- Fast Marching Energy CNN -- Deep Accurate Solver for the Geodesic Problem -- Deep Image Prior Regularized by Coupled Total Variation for Image Colorization -- Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras -- Latent-Space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data -- Natural Numerical Networks on Directed Graphs in Satellite Image Classification -- Piece-Wise Constant Image Segmentation with a Deep Image PriorApproach -- On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy -- Optimization for Imaging: Theory and Methods -- A Relaxed Proximal Gradient Descent Algorithm for Convergent Plug-and-Play with Proximal Denoiser -- Off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems -- Convergence Guarantees of Overparametrized Wide Deep Inverse Prior -- On the Remarkable Efficiency of SMART -- Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line -- A Quasi-Newton Primal-Dual Algorithm with Line Search -- Stochastic Gradient Descent for Linear Inverse Problems in Variable Exponent Lebesgue Spaces -- An Efficient Line Search for Sparse Reconstruction -- Learned Discretization Schemes for the Second-Order Total Generalized Variation -- Fluctuation-Based Deconvolution in Fluorescence Microscopy Using Plug-and-Play Denoisers -- Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization -- Scale Space, PDEs, Flow, Motion and Registration -- Geodesic Tracking of Retinal Vascular Trees with Optical and TV-Flow Enhancement in SE(2) -- Geometric Adaptations of PDE-G-CNNs -- The Variational Approach to the Flow of Sobolev-Diffeomorphisms Model -- Image Comparison and Scaling via Nonlinear Elasticity -- Learning Differential Invariants of Planar Curves -- Diffusion-Shock Inpainting -- Generalised Scale-Space Properties for Probabilistic Diffusion Models -- Gromov-Wasserstein Transfer Operators -- Optimal Transport Between GMM for Multiscale Texture Synthesis -- Asymptotic Result for a Decoupled Nonlinear Elasticity-Based Multiscale Registration Model -- Image Blending with Osmosis -- ?-Pixels for Hierarchical Analysis of Digital Objects -- Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks -- On Photometric Stereo in the Presence of a Refractive Interface -- Multi-View Normal Estimation ? Application to Slanted Plane-Sweeping -- Partial Shape Similarity by Multi-Metric Hamiltonian Spectra Matching -- Modeling Large-Scale Joint Distributions and Inference by Randomized Assignment -- Quantum State Assignment Flows. 330 $aThis book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which took place in Santa Margherita di Pula, Italy, in May 2023. The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: Theory and Methods; Scale Space, PDEs, Flow, Motion and Registration. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14009 606 $aComputer vision 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aMachine learning 606 $aComputer science$xMathematics 606 $aPattern recognition systems 606 $aComputer Vision 606 $aComputer Communication Networks 606 $aComputer Application in Social and Behavioral Sciences 606 $aMachine Learning 606 $aMathematics of Computing 606 $aAutomated Pattern Recognition 615 0$aComputer vision. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 0$aMachine learning. 615 0$aComputer science$xMathematics. 615 0$aPattern recognition systems. 615 14$aComputer Vision. 615 24$aComputer Communication Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aMachine Learning. 615 24$aMathematics of Computing. 615 24$aAutomated Pattern Recognition. 676 $a929.605 702 $aCalatroni$b Luca 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910725100603321 996 $aScale Space and Variational Methods in Computer Vision$94381134 997 $aUNINA