Energy Minimization Methods in Computer Vision and Pattern Recognition - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- Clustering and Quantum Methods -- Ising Models for Binary Clustering via Adiabatic Quantum Computing -- 1 Introduction -- 2 An Ising Model for Numerical Binary Clustering -- 2.1 The ``Standard'' k-Means Objective Function -- 2.2 An Alternative k-Means Objective Function -- 2.3 An Ising Model for k=2 Means Clustering -- 3 An Ising Model for Relational Binary Clustering -- 3.1 Normalized Cuts for Balanced Graph Clustering -- 3.2 An Ising Model for Balanced Graph Clustering -- 4 Adiabatic Quantum Binary Clustering -- 5 Practical Examples -- 5.1 Adiabatic Quantum k=2 Means Clustering -- 5.2 Adiabatic Quantum Graph Clustering -- 6 Related Work -- 7 Summary -- References -- Quantum Interference and Shape Detection -- 1 Introduction -- 1.1 Our Approach -- 1.2 Previous Work -- 1.3 Paper Organization -- 2 Energy Model for Shape Specification -- 2.1 Deformations -- 2.2 A Bayesian Model -- 3 Shape Dynamics -- 4 Quantum Shape Detection and Interference -- 4.1 Interference -- 4.2 Linear-Complexity Computation in the Size of the Data Set -- 4.3 A Note on Bayesian Theory -- 4.4 A Classical Statistical Version of the Quantum Criterion -- 4.5 Evaluation of and for Detection of the Center -- 5 Experiments with Center Detection -- 6 Analysis of the Circle Shape Center Detection -- 7 Conclusion -- References -- Structured Output Prediction and Learning for Deep Monocular 3D Human Pose Estimation -- 1 Introduction -- 2 Method -- 2.1 Quantized Regression for Depth Estimation -- 2.2 Efficient Optimization with Quadratic Pairwise Terms -- 2.3 Network Connectivity: From Star-Shaped to Loopy Graphs -- 2.4 Deeply Supervised 2D- and 3D-Learning -- 2.5 Training with a Structured Loss Function -- 3 Experimental Evaluation -- 3.1 Network Architecture -- 3.2 Dataset -- 3.3 Joint Training with 2D Pose -- 3.4 Results

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