| Título : |
8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings |
| Tipo de documento: |
documento electrónico |
| Autores: |
Elmoataz, Abderrahim, ; Fadili, Jalal, ; Quéau, Yvain, ; Rabin, Julien, ; Simon, Loïc, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2021 |
| Número de páginas: |
XIV, 580 p. 36 ilustraciones |
| ISBN/ISSN/DL: |
978-3-030-75549-2 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Visión por computador Red de computadoras Ciencias sociales Aprendizaje automático Informática Sistemas de reconocimiento de patrones Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento Matemáticas de la Computación Reconocimiento de patrones automatizado |
| Índice Dewey: |
006.37 Visión artificial |
| Resumen: |
Este libro constituye las actas de la 8.ª Conferencia Internacional sobre Espacio de Escala y Métodos Variacionales en Visión por Computador, SSVM 2021, que tuvo lugar del 16 al 20 de mayo de 2021. La conferencia estaba prevista para celebrarse en Cabourg, Francia, pero cambió a un formato en línea debido a la pandemia de COVID-19. Los 45 artículos incluidos en este volumen fueron cuidadosamente revisados y seleccionados de un total de 64 presentaciones. Se organizaron en secciones temáticas denominadas de la siguiente manera: espacio de escala y métodos de ecuaciones diferenciales parciales; flujo, movimiento y registro; teoría y métodos de optimización en imágenes; aprendizaje automático en imágenes; segmentación y etiquetado; restauración, reconstrucción e interpolación; y problemas inversos en imágenes. |
| Nota de contenido: |
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. |
| En línea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings [documento electrónico] / Elmoataz, Abderrahim, ; Fadili, Jalal, ; Quéau, Yvain, ; Rabin, Julien, ; Simon, Loïc, . - 1 ed. . - [s.l.] : Springer, 2021 . - XIV, 580 p. 36 ilustraciones. ISBN : 978-3-030-75549-2 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Visión por computador Red de computadoras Ciencias sociales Aprendizaje automático Informática Sistemas de reconocimiento de patrones Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento Matemáticas de la Computación Reconocimiento de patrones automatizado |
| Índice Dewey: |
006.37 Visión artificial |
| Resumen: |
Este libro constituye las actas de la 8.ª Conferencia Internacional sobre Espacio de Escala y Métodos Variacionales en Visión por Computador, SSVM 2021, que tuvo lugar del 16 al 20 de mayo de 2021. La conferencia estaba prevista para celebrarse en Cabourg, Francia, pero cambió a un formato en línea debido a la pandemia de COVID-19. Los 45 artículos incluidos en este volumen fueron cuidadosamente revisados y seleccionados de un total de 64 presentaciones. Se organizaron en secciones temáticas denominadas de la siguiente manera: espacio de escala y métodos de ecuaciones diferenciales parciales; flujo, movimiento y registro; teoría y métodos de optimización en imágenes; aprendizaje automático en imágenes; segmentación y etiquetado; restauración, reconstrucción e interpolación; y problemas inversos en imágenes. |
| Nota de contenido: |
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. |
| En línea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
|  |
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