Del 12679 i serien Lecture Notes in Computer Science

Scale Space and Variational Methods in Computer Vision

8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings

Häftad, Engelska, 2021

AvAbderrahim Elmoataz,Jalal Fadili,Yvain Quéau,Julien Rabin,Loïc Simon

709 kr

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This book constitutes the proceedings of the 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, which took place during May 16-20, 2021.

Produktinformation

Utgivningsdatum2021-04-30
Mått155 x 235 x 32 mm
Vikt890 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor580
FörlagSpringer Nature Switzerland AG
ISBN9783030755485

Tillhör följande kategorier

Scale Space and Partial Differential Equations Methods.- Scale-covariant and Scale-invariant Gaussian Derivative Networks.- Quantisation Scale-Spaces.- Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations.- Nonlinear Spectral Processing of Shapes via Zero-homogeneous Flows.- Total-Variation Mode Decomposition.- Fast Morphological Dilation and Erosion for Grey Scale Images Using the Fourier Transform.- Diffusion, Pre-Smoothing and Gradient Descent.- Local Culprits of Shape Complexity.- Extension of Mathematical Morphology in Riemannian Spaces.- Flow, Motion and Registration.- Multiscale Registration.- Challenges for Optical Flow Estimates in Elastography.- An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation.- Low-rank Registration of Images Captured Under Unknown, Varying Lighting.- Towards Efficient Time Stepping for Numerical Shape Correspondence.- First Order Locally Orderless Registration.- Optimization Theory and Methods in Imaging.- First Order Geometric Multilevel Optimization For Discrete Tomography.- Bregman Proximal Gradient Algorithms for Deep Matrix Factorization.- Hessian Initialization Strategies for L-BFGS Solving Non-linear Inverse Problems.- Inverse Scale Space Iterations for Non-Convex Variational Problems Using Functional Lifting.- A Scaled and Adaptive FISTA Algorithm for Signal-dependent Sparse Image Super-resolution Problems.- Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI.- Machine Learning in Imaging.- Wasserstein Generative Models for Patch-based Texture Synthesis.- Sketched Learning for Image Denoising.- Translating Numerical Concepts for PDEs into Neural Architectures.- CLIP: Cheap Lipschitz Training of Neural Networks.- Variational Models for Signal Processing with Graph Neural Networks.- Synthetic Imagesas a Regularity Prior for Image Restoration Neural Networks.- Geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation.- Learning Local Regularization for Variational Image Restoration.- Segmentation and Labelling.- On the Correspondence between Replicator Dynamics and Assignment Flows.- Learning Linear Assignment Flows for Image Labeling via Exponential Integration.- On the Geometric Mechanics of Assignment Flows for Metric Data Labeling.- A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration.- Restoration, Reconstruction and Interpolation.- Inpainting-based Video Compression in FullHD.- Sparsity-aided Variational Mesh Restoration.- Lossless PDE-based Compression of 3D Medical Images.- Splines for Image Metamorphosis.- Residual Whiteness Principle for Automatic Parameter Selection in `2-`2 Image Super-resolution Problems.- Inverse Problems in Imaging.- Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy.- GMM-based Simultaneous Reconstruction and Segmentation in X-ray CT application.- Phase Retrieval via Polarization in Dynamical Sampling.- Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence.- Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems.- Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems.- Multi-frame Super-resolution from Noisy Data.