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Scale Space and Variational Methods in Computer Vision : 8th International Conference, SSVM 2021, Virtual Event, May 16-20, 2021, Proceedings / edited by Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12679
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12679
- Language:
- English
- Subjects (All):
- Computer vision.
- Computer networks.
- Social sciences-Data processing.
- Machine learning.
- Computer science-Mathematics.
- Pattern recognition systems.
- Computer Vision.
- Computer Communication Networks.
- Computer Application in Social and Behavioral Sciences.
- Machine Learning.
- Mathematics of Computing.
- Automated Pattern Recognition.
- Local Subjects:
- Computer Vision.
- Computer Communication Networks.
- Computer Application in Social and Behavioral Sciences.
- Machine Learning.
- Mathematics of Computing.
- Automated Pattern Recognition.
- Physical Description:
- 1 online resource (XIV, 580 pages) : 36 illustrations
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- 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. The conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic. The 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. They were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging. .
- Contents:
- 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 Images as 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-75549-2
- 9783030755492
- Access Restriction:
- Restricted for use by site license.
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