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Produktbild: Scale Space and Variational Methods in Computer Vision
Band 12679

Scale Space and Variational Methods in Computer Vision 8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings

Aus der Reihe Lecture Notes in Computer Science

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.04.2021

Abbildungen

XIV, 580 p. 36 illus.

Herausgeber

Abderrahim Elmoataz + weitere

Verlag

Springer

Seitenzahl

580

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

890 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75548-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.04.2021

Abbildungen

XIV, 580 p. 36 illus.

Herausgeber

Verlag

Springer

Seitenzahl

580

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

890 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75548-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Scale Space and Variational Methods in Computer Vision

  • Scale
    Space and Partial Di
    ff
    erential 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.