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Scale Space and Variational Methods in Computer Vision

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Cover of 'Scale Space and Variational Methods in Computer Vision'

Table of Contents

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    Book Overview
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    Chapter 1 Spatio-Temporal Scale Selection in Video Data
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    Chapter 2 Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters
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    Chapter 3 Corner Detection Using the Affine Morphological Scale Space
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    Chapter 4 Nonlinear Spectral Image Fusion
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    Chapter 5 Tubular Structure Segmentation Based on Heat Diffusion
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    Chapter 6 Analytic Existence and Uniqueness Results for PDE-Based Image Reconstruction with the Laplacian
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    Chapter 7 Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
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    Chapter 8 An Efficient and Stable Two-Pixel Scheme for 2D Forward-and-Backward Diffusion
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    Chapter 9 Blind Space-Variant Single-Image Restoration of Defocus Blur
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    Chapter 10 Denoising by Inpainting
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    Chapter 11 Stochastic Image Reconstruction from Local Histograms of Gradient Orientation
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    Chapter 12 A Dynamic Programming Solution to Bounded Dejittering Problems
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    Chapter 13 Robust Blind Deconvolution with Convolution-Spectrum-Based Kernel Regulariser and Poisson-Noise Data Terms
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    Chapter 14 Optimal Patch Assignment for Statistically Constrained Texture Synthesis
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    Chapter 15 A Correlation-Based Dissimilarity Measure for Noisy Patches
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    Chapter 16 Analysis of a Physically Realistic Film Grain Model, and a Gaussian Film Grain Synthesis Algorithm
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    Chapter 17 Below the Surface of the Non-local Bayesian Image Denoising Method
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    Chapter 18 Directional Total Generalized Variation Regularization for Impulse Noise Removal
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    Chapter 19 A Novel Convex Relaxation for Non-binary Discrete Tomography
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    Chapter 20 Image Reconstruction by Multilabel Propagation
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    Chapter 21 User-Friendly Simultaneous Tomographic Reconstruction and Segmentation with Class Priors
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    Chapter 22 An Optimal Transport-Based Restoration Method for Q-Ball Imaging
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    Chapter 23 Nonlinear Flows for Displacement Correction and Applications in Tomography
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    Chapter 24 Performance Bounds for Cosparse Multichannel Signal Recovery via Collaborative-TV
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    Chapter 25 Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data
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    Chapter 26 Graphical Model Parameter Learning by Inverse Linear Programming
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    Chapter 27 A Fast MBO Scheme for Multiclass Data Classification
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    Chapter 28 Convex Non-Convex Segmentation over Surfaces
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    Chapter 29 Numerical Integration of Riemannian Gradient Flows for Image Labeling
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    Chapter 30 MAP Image Labeling Using Wasserstein Messages and Geometric Assignment
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    Chapter 31 Multinomial Level-Set Framework for Multi-region Image Segmentation
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    Chapter 32 Local Mean Multiphase Segmentation with HMMF Models
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    Chapter 33 An Efficient Lagrangian Algorithm for an Anisotropic Geodesic Active Contour Model
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    Chapter 34 A Probabilistic Framework for Curve Evolution
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    Chapter 35 Denoising of Image Gradients and Constrained Total Generalized Variation
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    Chapter 36 Infimal Convolution Coupling of First and Second Order Differences on Manifold-Valued Images
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    Chapter 37 Optimal Transport for Manifold-Valued Images
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    Chapter 38 Time Discrete Extrapolation in a Riemannian Space of Images
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    Chapter 39 On a Projected Weiszfeld Algorithm
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    Chapter 40 A Unified Framework for the Restoration of Images Corrupted by Additive White Noise
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    Chapter 41 Learning Filter Functions in Regularisers by Minimising Quotients
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    Chapter 42 Bregman-Proximal Augmented Lagrangian Approach to Multiphase Image Segmentation
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    Chapter 43 A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow
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    Chapter 44 Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between
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    Chapter 45 Transport Based Image Morphing with Intensity Modulation
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    Chapter 46 Vehicle X-ray Scans Registration: A One-Dimensional Optimization Problem
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    Chapter 47 Evaluating Data Terms for Variational Multi-frame Super-Resolution
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    Chapter 48 Compressed Motion Sensing
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    Chapter 49 A Unified Hyperelastic Joint Segmentation/Registration Model Based on Weighted Total Variation and Nonlocal Shape Descriptors
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    Chapter 50 Adaptive Discretizations for Non-smooth Variational Vision
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    Chapter 51 The Hessian of Axially Symmetric Functions on SE(3) and Application in 3D Image Analysis
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    Chapter 52 Semi-calibrated Near-Light Photometric Stereo
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    Chapter 53 Shape Matching by Time Integration of Partial Differential Equations
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    Chapter 54 Subspace Least Squares Multidimensional Scaling
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    Chapter 55 Beyond Multi-view Stereo: Shading-Reflectance Decomposition
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Title
Scale Space and Variational Methods in Computer Vision
Published by
Springer International Publishing, May 2017
DOI 10.1007/978-3-319-58771-4
ISBNs
978-3-31-958770-7, 978-3-31-958771-4
Editors

Lauze, François, Dong, Yiqiu, Dahl, Anders Bjorholm

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Other 2 22%
Professor > Associate Professor 1 11%
Student > Master 1 11%
Unknown 2 22%
Readers by discipline Count As %
Computer Science 4 44%
Engineering 2 22%
Unknown 3 33%

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