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Energy Minimization Methods in Computer Vision and Pattern Recognition : 10th International Conference, EMMCVPR 2015, Hong Kong, China, January 13-16, 2015. Proceedings

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Cover of 'Energy Minimization Methods in Computer Vision and Pattern Recognition : 10th International Conference, EMMCVPR 2015, Hong Kong, China, January 13-16, 2015. Proceedings'

Table of Contents

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    Book Overview
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    Chapter 1 Convex Envelopes for Low Rank Approximation
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    Chapter 2 Maximizing Flows with Message-Passing: Computing Spatially Continuous Min-Cuts
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    Chapter 3 A Compact Linear Programming Relaxation for Binary Sub-modular MRF
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    Chapter 4 On the Link between Gaussian Homotopy Continuation and Convex Envelopes
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    Chapter 5 How Hard Is the LP Relaxation of the Potts Min-Sum Labeling Problem?
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    Chapter 6 Coarse-to-Fine Minimization of Some Common Nonconvexities
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    Chapter 7 Why Does Non-binary Mask Optimisation Work for Diffusion-Based Image Compression?
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    Chapter 8 Expected Patch Log Likelihood with a Sparse Prior
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    Chapter 9 Blind Deconvolution via Lower-Bounded Logarithmic Image Priors
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    Chapter 10 Low Rank Priors for Color Image Regularization
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    Chapter 11 A Novel Framework for Nonlocal Vectorial Total Variation Based on ℓ p , q , r  −norms
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    Chapter 12 Inpainting of Cyclic Data Using First and Second Order Differences
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    Chapter 13 Discrete Green’s Functions for Harmonic and Biharmonic Inpainting with Sparse Atoms
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    Chapter 14 A Fast Projection Method for Connectivity Constraints in Image Segmentation
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    Chapter 15 Two-Dimensional Variational Mode Decomposition
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    Chapter 16 Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme
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    Chapter 17 A Novel Active Contour Model for Texture Segmentation
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    Chapter 18 Segmentation Using SubMarkov Random Walk
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    Chapter 19 Automatic Shape Constraint Selection Based Object Segmentation
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    Chapter 20 Justifying Tensor-Driven Diffusion from Structure-Adaptive Statistics of Natural Images
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    Chapter 21 Variational Time-Implicit Multiphase Level-Sets
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    Chapter 22 An Efficient Curve Evolution Algorithm for Multiphase Image Segmentation
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    Chapter 23 A Tensor Variational Formulation of Gradient Energy Total Variation
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    Chapter 24 Color Image Segmentation by Minimal Surface Smoothing
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    Chapter 25 Domain Decomposition Methods for Total Variation Minimization
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    Chapter 26 A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method
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    Chapter 27 Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames
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    Chapter 28 Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV
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    Chapter 29 Point Sets Matching by Feature-Aware Mixture Point Matching Algorithm
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    Chapter 30 Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions
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    Chapter 31 Mapping the Energy Landscape of Non-convex Optimization Problems
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    Chapter 32 Marked Point Process Model for Curvilinear Structures Extraction
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    Chapter 33 Randomly Walking Can Get You Lost: Graph Segmentation with Unknown Edge Weights
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    Chapter 34 Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images
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    Chapter 35 A Technique for Lung Nodule Candidate Detection in CT Using Global Minimization Methods
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    Chapter 36 Hierarchical Planar Correlation Clustering for Cell Segmentation
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Title
Energy Minimization Methods in Computer Vision and Pattern Recognition : 10th International Conference, EMMCVPR 2015, Hong Kong, China, January 13-16, 2015. Proceedings
Published by
Springer International Publishing, January 2015
DOI 10.1007/978-3-319-14612-6
ISBNs
978-3-31-914611-9, 978-3-31-914612-6
Editors

Tai, Xue-Cheng, Lysaker, Marius, Chan, Tony F., Bae, Egil

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The data shown below were collected from the profiles of 2 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Researcher 2 17%
Professor > Associate Professor 1 8%
Professor 1 8%
Unknown 4 33%
Readers by discipline Count As %
Computer Science 2 17%
Engineering 2 17%
Psychology 1 8%
Arts and Humanities 1 8%
Social Sciences 1 8%
Other 1 8%
Unknown 4 33%