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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 The Fractional Harris-Laplace Feature Detector
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    Chapter 2 Macrocanonical Models for Texture Synthesis
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    Chapter 3 Finding Structure in Point Cloud Data with the Robust Isoperimetric Loss
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    Chapter 4 Deep Eikonal Solvers
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    Chapter 5 A Splitting-Based Algorithm for Multi-view Stereopsis of Textureless Objects
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    Chapter 6 Pseudodifferential Inpainting: The Missing Link Between PDE- and RBF-Based Interpolation
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    Chapter 7 Towards PDE-Based Video Compression with Optimal Masks and Optic Flow
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    Chapter 8 Compressing Audio Signals with Inpainting-Based Sparsification
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    Chapter 9 Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies
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    Chapter 10 Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems
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    Chapter 11 Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties
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    Chapter 12 An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data
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    Chapter 13 A New Iterative Method for CT Reconstruction with Uncertain View Angles
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    Chapter 14 Time Discrete Geodesics in Deep Feature Spaces for Image Morphing
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    Chapter 15 Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs
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    Chapter 16 The Convex-Hull-Stripping Median Approximates Affine Curvature Motion
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    Chapter 17 Total Variation and Mean Curvature PDEs on the Space of Positions and Orientations
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    Chapter 18 A Variational Convex Hull Algorithm
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    Chapter 19 PDE Evolutions for M-Smoothers: From Common Myths to Robust Numerics
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    Chapter 20 Variational Registration of Multiple Images with the SVD Based $$\mathrm {S}q\mathrm {N}$$ Distance Measure
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    Chapter 21 Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
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    Chapter 22 Variational Image Registration for Inhomogeneous-Resolution Pairs
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    Chapter 23 Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
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    Chapter 24 Sparsification Scale-Spaces
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    Chapter 25 Stable Explicit p-Laplacian Flows Based on Nonlinear Eigenvalue Analysis
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    Chapter 26 Provably Scale-Covariant Networks from Oriented Quasi Quadrature Measures in Cascade
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    Chapter 27 A Fast Multi-layer Approximation to Semi-discrete Optimal Transport
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    Chapter 28 Global Similarity with Additive Smoothness for Spectral Segmentation
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    Chapter 29 Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers
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    Chapter 30 Lattice Metric Space Application to Grain Defect Detection
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    Chapter 31 Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
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    Chapter 32 Direct MRI Segmentation from k-Space Data by Iterative Potts Minimization
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    Chapter 33 A Balanced Phase Field Model for Active Contours
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    Chapter 34 Unsupervised Labeling by Geometric and Spatially Regularized Self-assignment
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    Chapter 35 Aorta Centerline Smoothing and Registration Using Variational Models
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    Chapter 36 A Connection Between Image Processing and Artificial Neural Networks Layers Through a Geometric Model of Visual Perception
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    Chapter 37 A Cortical-Inspired Model for Orientation-Dependent Contrast Perception: A Link with Wilson-Cowan Equations
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    Chapter 38 A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions
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    Chapter 39 A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy
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    Chapter 40 Preservation of Piecewise Constancy under TV Regularization with Rectilinear Anisotropy
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    Chapter 41 Total Directional Variation for Video Denoising
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    Chapter 42 Joint CNN and Variational Model for Fully-Automatic Image Colorization
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    Chapter 43 A Variational Perspective on the Assignment Flow
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    Chapter 44 Functional Liftings of Vectorial Variational Problems with Laplacian Regularization
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Mentioned by

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Readers on

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Title
Scale Space and Variational Methods in Computer Vision
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-22368-7
ISBNs
978-3-03-022367-0, 978-3-03-022368-7
Editors

Jan Lellmann, Martin Burger, Jan Modersitzki

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 2 2%
Researcher 1 <1%
Professor 1 <1%
Other 1 <1%
Unknown 96 95%
Readers by discipline Count As %
Computer Science 3 3%
Engineering 1 <1%
Unknown 97 96%