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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 Ising Models for Binary Clustering via Adiabatic Quantum Computing
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    Chapter 2 Quantum Interference and Shape Detection
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    Chapter 3 Structured Output Prediction and Learning for Deep Monocular 3D Human Pose Estimation
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    Chapter 4 Dominant Set Biclustering
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    Chapter 5 Bragg Diffraction Patterns as Graph Characteristics
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    Chapter 6 Variational Large Displacement Optical Flow Without Feature Matches
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    Chapter 7 Temporal Semantic Motion Segmentation Using Spatio Temporal Optimization
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    Chapter 8 Depth-Adaptive Computational Policies for Efficient Visual Tracking
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    Chapter 9 Multiframe Motion Coupling for Video Super Resolution
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    Chapter 10 Illumination-Aware Large Displacement Optical Flow
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    Chapter 11 Location Uncertainty Principle: Toward the Definition of Parameter-Free Motion Estimators
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    Chapter 12 Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization
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    Chapter 13 Nonlinear Compressed Sensing for Multi-emitter X-Ray Imaging
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    Chapter 14 Unified Functional Framework for Restoration of Image Sequences Degraded by Atmospheric Turbulence
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    Chapter 15 A Convex Approach to K-Means Clustering and Image Segmentation
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    Chapter 16 Luminance-Guided Chrominance Denoising with Debiased Coupled Total Variation
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    Chapter 17 Optimizing Wavelet Bases for Sparser Representations
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    Chapter 18 Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
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    Chapter 19 Vehicle X-Ray Images Registration
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    Chapter 20 Superpixels Optimized by Color and Shape
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    Chapter 21 Maximum Consensus Parameter Estimation by Reweighted $$\ell _1$$ ℓ 1 Methods
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    Chapter 22 A Graph Theoretic Approach for Shape from Shading
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    Chapter 23 A Variational Approach to Shape-from-Shading Under Natural Illumination
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    Chapter 24 Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm
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    Chapter 25 Inverse Lightfield Rendering for Shape, Reflection and Natural Illumination
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    Chapter 26 Shadow and Specularity Priors for Intrinsic Light Field Decomposition
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    Chapter 27 Modelling Stable Backward Diffusion and Repulsive Swarms with Convex Energies and Range Constraints
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    Chapter 28 Euler-Lagrange Network Dynamics
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    Chapter 29 Slack and Margin Rescaling as Convex Extensions of Supermodular Functions
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    Chapter 30 Multi-object Convexity Shape Prior for Segmentation
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    Chapter 31 Fast Asymmetric Fronts Propagation for Voronoi Region Partitioning and Image Segmentation
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    Chapter 32 PointFlow: A Model for Automatically Tracing Object Boundaries and Inferring Illusory Contours
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    Chapter 33 An Isotropic Minimal Path Based Framework for Segmentation and Quantification of Vascular Networks
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    Chapter 34 Limited-Memory Belief Propagation via Nested Optimization
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    Chapter 35 Geometric Image Labeling with Global Convex Labeling Constraints
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    Chapter 36 Discretized Convex Relaxations for the Piecewise Smooth Mumford-Shah Model
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    Chapter 37 A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials
Attention for Chapter 24: Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm
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Chapter title
Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm
Chapter number 24
Book title
Energy Minimization Methods in Computer Vision and Pattern Recognition
Published by
Springer, Cham, October 2017
DOI 10.1007/978-3-319-78199-0_24
Book ISBNs
978-3-31-978198-3, 978-3-31-978199-0
Authors

Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 50%
Student > Ph. D. Student 1 25%
Professor 1 25%
Readers by discipline Count As %
Unspecified 3 75%
Engineering 1 25%