Scale Space and Variational Methods in Computer Vision

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

AvAbderrahim Elmoataz,Jalal Fadili

Häftad, Engelska, 2021

Del 12679 i serien Lecture Notes in Computer Science

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Beskrivning

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

Utgivningsdatum:2021-04-30
Mått:155 x 235 x 32 mm
Vikt:890 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:580
Förlag:Springer Nature Switzerland AG
ISBN:9783030755485

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Innehållsförteckning

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.