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001467887 001__ 1467887
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001467887 020__ $$a9783031319754$$q(electronic bk.)
001467887 020__ $$a3031319753$$q(electronic bk.)
001467887 020__ $$z3031319745
001467887 020__ $$z9783031319747
001467887 0247_ $$a10.1007/978-3-031-31975-4$$2doi
001467887 035__ $$aSP(OCoLC)1379017738
001467887 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP
001467887 049__ $$aISEA
001467887 050_4 $$aTA1634
001467887 08204 $$a006.6$$223/eng/20230522
001467887 1112_ $$aSSVM (Conference)$$n(9th :$$d2023 :$$cSanta Margherita di Pula, Italy)
001467887 24510 $$aScale space and variational methods in computer vision :$$b9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21-25, 2023 proceedings /$$cLuca Calatroni, Marco Donatelli, Serena Morigi, Marco Prato, Matteo Santacesaria, editors.
001467887 2463_ $$aSSVM 2023
001467887 260__ $$aCham, Switzerland :$$bSpringer,$$c2023.
001467887 300__ $$a1 online resource
001467887 4901_ $$aLecture notes in computer science ;$$v14009
001467887 500__ $$aIncludes author index.
001467887 5050_ $$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.
001467887 506__ $$aAccess limited to authorized users.
001467887 520__ $$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.
001467887 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 22, 2023).
001467887 650_0 $$aComputer vision$$vCongresses.
001467887 655_0 $$aElectronic books.
001467887 7001_ $$aCalatroni, Luca.
001467887 7001_ $$aMorigi, Serena.
001467887 7001_ $$aDonatelli, Marco.
001467887 7001_ $$aPrato, Marco.
001467887 7001_ $$aSantacesaria, Matteo.
001467887 77608 $$iPrint version: $$z3031319745$$z9783031319747$$w(OCoLC)1374242978
001467887 830_0 $$aLecture notes in computer science ;$$v14009.
001467887 852__ $$bebk
001467887 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31975-4$$zOnline Access$$91397441.1
001467887 909CO $$ooai:library.usi.edu:1467887$$pGLOBAL_SET
001467887 980__ $$aBIB
001467887 980__ $$aEBOOK
001467887 982__ $$aEbook
001467887 983__ $$aOnline
001467887 994__ $$a92$$bISE