LEADER 06861nam 22007455 450 001 9910483096403321 005 20251113183642.0 010 $a3-030-75549-5 024 7 $a10.1007/978-3-030-75549-2 035 $a(CKB)4100000011912211 035 $a(MiAaPQ)EBC6578620 035 $a(Au-PeEL)EBL6578620 035 $a(OCoLC)1249507995 035 $a(PPN)255289510 035 $a(DE-He213)978-3-030-75549-2 035 $a(EXLCZ)994100000011912211 100 $a20210429d2021 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 $e8th International Conference, SSVM 2021, Virtual Event, May 16?20, 2021, Proceedings /$fedited by Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (584 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12679 311 08$a3-030-75548-7 320 $aIncludes bibliographical references and index. 327 $aScale 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. 330 $aThis book constitutes the proceedings of the 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, which took place during May 16-20, 2021. The conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic. The 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. They were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging. . 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12679 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 $a006.37 702 $aElmoataz$b Abderrahim 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483096403321 996 $aScale Space and Variational Methods in Computer Vision$94381134 997 $aUNINA