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Scale Space and Variational Methods in Computer Vision

Overview of attention for book
Scale Space and Variational Methods in Computer Vision
Springer, Berlin, Heidelberg

Table of Contents

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    Book Overview
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    Chapter 1 Targeted Iterative Filtering
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    Chapter 2 Generalized Gradient on Vector Bundle – Application to Image Denoising
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    Chapter 3 Expert Regularizers for Task Specific Processing
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    Chapter 4 A Spectral Approach to Total Variation
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    Chapter 5 Convex Generalizations of Total Variation Based on the Structure Tensor with Applications to Inverse Problems
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    Chapter 6 Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities
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    Chapter 7 Variational Methods for Motion Deblurring with Still Background
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    Chapter 8 Blind Deblurring Using a Simplified Sharpness Index
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    Chapter 9 A Cascadic Alternating Krylov Subspace Image Restoration Method
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    Chapter 10 B-SMART: Bregman-Based First-Order Algorithms for Non-negative Compressed Sensing Problems
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    Chapter 11 Epigraphical Projection for Solving Least Squares Anscombe Transformed Constrained Optimization Problems
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    Chapter 12 Static and Dynamic Texture Mixing Using Optimal Transport
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    Chapter 13 Scale Space and Variational Methods in Computer Vision
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    Chapter 14 Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
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    Chapter 15 A Fast Algorithm for Exact Histogram Specification. Simple Extension to Colour Images
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    Chapter 16 Constrained Sparse Texture Synthesis
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    Chapter 17 Outlier Removal Power of the L1-Norm Super-Resolution
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    Chapter 18 Why Is the Census Transform Good for Robust Optic Flow Computation?
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    Chapter 19 Generalised Perspective Shape from Shading in Spherical Coordinates
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    Chapter 20 Weighted Patch-Based Reconstruction: Linking (Multi-view) Stereo to Scale Space
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    Chapter 21 Scale Space and Variational Methods in Computer Vision
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    Chapter 22 Perspective Photometric Stereo with Shadows
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    Chapter 23 Solving the Uncalibrated Photometric Stereo Problem Using Total Variation
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    Chapter 24 Scale Space and Variational Methods in Computer Vision
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    Chapter 25 A Mathematically Justified Algorithm for Shape from Texture
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    Chapter 26 Scale Space and Variational Methods in Computer Vision
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    Chapter 27 Compression of Depth Maps with Segment-Based Homogeneous Diffusion
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    Chapter 28 Scale Space Operators on Hierarchies of Segmentations
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    Chapter 29 Discrete Deep Structure
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    Chapter 30 Image Matching Using Generalized Scale-Space Interest Points
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    Chapter 31 A Fully Discrete Theory for Linear Osmosis Filtering
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    Chapter 32 L 2 -Stable Nonstandard Finite Differences for Anisotropic Diffusion
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    Chapter 33 Relations between Amoeba Median Algorithms and Curvature-Based PDEs
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    Chapter 34 Scale and Edge Detection with Topological Derivatives
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    Chapter 35 Active Contours for Multi-region Image Segmentation with a Single Level Set Function
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    Chapter 36 Regularized Discrete Optimal Transport
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    Chapter 37 Variational Method for Computing Average Images of Biological Organs
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    Chapter 38 A Hierarchical Approach to Optimal Transport
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    Chapter 39 Layered Mean Shift Methods
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    Chapter 40 Partial Optimality via Iterative Pruning for the Potts Model
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    Chapter 41 Wimmelbild Analysis with Approximate Curvature Coding Distance Images
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    Chapter 42 Defect Classification on Specular Surfaces Using Wavelets
Overall attention for this book and its chapters
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Title
Scale Space and Variational Methods in Computer Vision
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-38267-3
ISBNs
978-3-64-238266-6, 978-3-64-238267-3
Editors

Arjan Kuijper, Kristian Bredies, Thomas Pock, Horst Bischof

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Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 100%
Readers by discipline Count As %
Unspecified 1 100%

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