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Energy Minimization Methods in Computer Vision and Pattern Recognition

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Cover of 'Energy Minimization Methods in Computer Vision and Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Rapid Mode Estimation for 3D Brain MRI Tumor Segmentation
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    Chapter 2 Energy Minimization Methods in Computer Vision and Pattern Recognition
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    Chapter 3 Linear Osmosis Models for Visual Computing
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    Chapter 4 Analysis of Bayesian Blind Deconvolution
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    Chapter 5 A Variational Method for Expanding the Bit-Depth of Low Contrast Image
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    Chapter 6 Energy Minimization Methods in Computer Vision and Pattern Recognition
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    Chapter 7 Simultaneous Fusion Moves for 3D-Label Stereo
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    Chapter 8 Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints
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    Chapter 9 Discrete Geodesic Regression in Shape Space
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    Chapter 10 Object Segmentation by Shape Matching with Wasserstein Modes
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    Chapter 11 Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data
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    Chapter 12 An Optimal Control Approach to Find Sparse Data for Laplace Interpolation
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    Chapter 13 Curvature Regularization for Resolution-Independent Images
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    Chapter 14 PoseField: An Efficient Mean-Field Based Method for Joint Estimation of Human Pose, Segmentation, and Depth
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    Chapter 15 Semantic Video Segmentation from Occlusion Relations within a Convex Optimization Framework
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    Chapter 16 A Co-occurrence Prior for Continuous Multi-label Optimization
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    Chapter 17 Convex Relaxations for a Generalized Chan-Vese Model
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    Chapter 18 Multiclass Segmentation by Iterated ROF Thresholding
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    Chapter 19 A Generic Convexification and Graph Cut Method for Multiphase Image Segmentation
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    Chapter 20 Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs
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    Chapter 21 Contour-Relaxed Superpixels
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    Chapter 22 Sparse-MIML: A Sparsity-Based Multi-Instance Multi-Learning Algorithm
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    Chapter 23 Consensus Clustering with Robust Evidence Accumulation
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    Chapter 24 Variational Image Segmentation and Cosegmentation with the Wasserstein Distance
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    Chapter 25 A Convex Formulation for Global Histogram Based Binary Segmentation
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    Chapter 26 A Continuous Shape Prior for MRF-Based Segmentation
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Title
Energy Minimization Methods in Computer Vision and Pattern Recognition
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-40395-8
ISBNs
978-3-64-240394-1, 978-3-64-240395-8
Editors

Anders Heyden, Fredrik Kahl, Carl Olsson, Magnus Oskarsson, Xue-Cheng Tai

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The data shown below were collected from the profile of 1 X user 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 14%
Unknown 6 86%
Readers by discipline Count As %
Engineering 1 14%
Unknown 6 86%