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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
  2. Altmetric Badge
    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 32: PointFlow: A Model for Automatically Tracing Object Boundaries and Inferring Illusory Contours
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Chapter title
PointFlow: A Model for Automatically Tracing Object Boundaries and Inferring Illusory Contours
Chapter number 32
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_32
Book ISBNs
978-3-31-978198-3, 978-3-31-978199-0

Fang Yang, Alfred M. Bruckstein, Laurent D. Cohen

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Librarian 1 33%
Student > Ph. D. Student 1 33%
Researcher 1 33%
Readers by discipline Count As %
Arts and Humanities 1 33%
Mathematics 1 33%
Engineering 1 33%