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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

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation
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    Chapter 2 Tubular Anisotropy Segmentation
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    Chapter 3 An Unconstrained Multiphase Thresholding Approach for Image Segmentation
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    Chapter 4 Extraction of the Intercellular Skeleton from 2D Images of Embryogenesis Using Eikonal Equation and Advective Subjective Surface Method
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    Chapter 5 On Level-Set Type Methods for Recovering Piecewise Constant Solutions of Ill-Posed Problems
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    Chapter 6 The Nonlinear Tensor Diffusion in Segmentation of Meaningful Biological Structures from Image Sequences of Zebrafish Embryogenesis
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    Chapter 7 Composed Segmentation of Tubular Structures by an Anisotropic PDE Model
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    Chapter 8 Extrapolation of Vector Fields Using the Infinity Laplacian and with Applications to Image Segmentation
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    Chapter 9 A Schrödinger Equation for the Fast Computation of Approximate Euclidean Distance Functions
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    Chapter 10 Semi-supervised Segmentation Based on Non-local Continuous Min-Cut
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    Chapter 11 Momentum Based Optimization Methods for Level Set Segmentation
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    Chapter 12 Optimization of Divergences within the Exponential Family for Image Segmentation
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    Chapter 13 Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
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    Chapter 14 Geodesically Linked Active Contours: Evolution Strategy Based on Minimal Paths
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    Chapter 15 Validation of Watershed Regions by Scale-Space Statistics
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    Chapter 16 Adaptation of Eikonal Equation over Weighted Graph
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    Chapter 17 A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking
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    Chapter 18 A Nonlinear Probabilistic Curvature Motion Filter for Positron Emission Tomography Images
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    Chapter 19 Finsler Geometry on Higher Order Tensor Fields and Applications to High Angular Resolution Diffusion Imaging
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    Chapter 20 Bregman-EM-TV Methods with Application to Optical Nanoscopy
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    Chapter 21 PDE-Driven Adaptive Morphology for Matrix Fields
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    Chapter 22 On Semi-implicit Splitting Schemes for the Beltrami Color Flow
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    Chapter 23 Scale Space and Variational Methods in Computer Vision
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    Chapter 24 Multiplicative Noise Cleaning via a Variational Method Involving Curvelet Coefficients
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    Chapter 25 Projected Gradient Based Color Image Decomposition
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    Chapter 26 A Dual Formulation of the TV-Stokes Algorithm for Image Denoising
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    Chapter 27 Anisotropic Regularization for Inverse Problems with Application to the Wiener Filter with Gaussian and Impulse Noise
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    Chapter 28 Locally Adaptive Total Variation Regularization
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    Chapter 29 Basic Image Features (BIFs) Arising from Approximate Symmetry Type
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    Chapter 30 An Anisotropic Fourth-Order Partial Differential Equation for Noise Removal
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    Chapter 31 Enhancement of Blurred and Noisy Images Based on an Original Variant of the Total Variation
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    Chapter 32 Coarse-to-Fine Image Reconstruction Based on Weighted Differential Features and Background Gauge Fields
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    Chapter 33 Edge-Enhanced Image Reconstruction Using (TV) Total Variation and Bregman Refinement
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    Chapter 34 Nonlocal Variational Image Deblurring Models in the Presence of Gaussian or Impulse Noise
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    Chapter 35 A Geometric PDE for Interpolation of M -Channel Data
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    Chapter 36 An Edge-Preserving Multilevel Method for Deblurring, Denoising, and Segmentation
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    Chapter 37 Fast Dejittering for Digital Video Frames
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    Chapter 38 Scale Space and Variational Methods in Computer Vision
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    Chapter 39 Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage
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    Chapter 40 Anisotropic Smoothing Using Double Orientations
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    Chapter 41 Image Denoising Using TV-Stokes Equation with an Orientation-Matching Minimization
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    Chapter 42 Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
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    Chapter 43 The Convergence of a Central-Difference Discretization of Rudin-Osher-Fatemi Model for Image Denoising
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    Chapter 44 Theoretical Foundations for Discrete Forward-and-Backward Diffusion Filtering
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    Chapter 45 L 0 -Norm and Total Variation for Wavelet Inpainting
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    Chapter 46 Total-Variation Based Piecewise Affine Regularization
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    Chapter 47 Image Denoising by Harmonic Mean Curvature Flow
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    Chapter 48 Tracking Closed Curves with Non-linear Stochastic Filters
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    Chapter 49 A Multi-scale Feature Based Optic Flow Method for 3D Cardiac Motion Estimation
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    Chapter 50 A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother
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    Chapter 51 A Scale-Space Approach to Landmark Constrained Image Registration
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    Chapter 52 A Variational Approach for Volume-to-Slice Registration
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    Chapter 53 Hyperbolic Numerics for Variational Approaches to Correspondence Problems
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    Chapter 54 From a Single Point to a Surface Patch by Growing Minimal Paths
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    Chapter 55 Scale Space and Variational Methods in Computer Vision
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    Chapter 56 An Implicit Method for Interpolating Two Digital Closed Curves on Parallel Planes
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    Chapter 57 Pose Invariant Shape Prior Segmentation Using Continuous Cuts and Gradient Descent on Lie Groups
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    Chapter 58 A Non-local Approach to Shape from Ambient Shading
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    Chapter 59 An Elasticity Approach to Principal Modes of Shape Variation
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    Chapter 60 Pre-image as Karcher Mean Using Diffusion Maps: Application to Shape and Image Denoising
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    Chapter 61 Fast Shape from Shading for Phong-Type Surfaces
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    Chapter 62 Generic Scene Recovery Using Multiple Images
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    Chapter 63 Highly Accurate PDE-Based Morphology for General Structuring Elements
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    Chapter 64 Computational Geometry-Based Scale-Space and Modal Image Decomposition
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    Chapter 65 Highlight on a Feature Extracted at Fine Scales: The Pointwise Lipschitz Regularity
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    Chapter 66 Line Enhancement and Completion via Linear Left Invariant Scale Spaces on SE (2)
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    Chapter 67 Spatio-Featural Scale-Space
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    Chapter 68 Scale Spaces on the 3D Euclidean Motion Group for Enhancement of HARDI Data
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    Chapter 69 On the Rate of Structural Change in Scale Spaces
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    Chapter 70 Transitions of a Multi-scale Image Hierarchy Tree
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    Chapter 71 Local Scale Measure for Remote Sensing Images
Attention for Chapter 42: Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
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Chapter title
Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
Chapter number 42
Book title
Scale Space and Variational Methods in Computer Vision
Published by
Springer, Berlin, Heidelberg, June 2009
DOI 10.1007/978-3-642-02256-2_42
Book ISBNs
978-3-64-202255-5, 978-3-64-202256-2
Authors

Xue-Cheng Tai, Chunlin Wu, Tai, Xue-Cheng, Wu, Chunlin

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Germany 2 3%
Brazil 1 1%
France 1 1%
China 1 1%
Sweden 1 1%
Unknown 70 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 35%
Researcher 19 24%
Professor 5 6%
Other 5 6%
Student > Postgraduate 4 5%
Other 11 14%
Unknown 8 10%
Readers by discipline Count As %
Mathematics 29 36%
Computer Science 22 28%
Engineering 15 19%
Physics and Astronomy 3 4%
Social Sciences 1 1%
Other 1 1%
Unknown 9 11%