Energy Minimization Methods in Computer Vision and Pattern Recognition

Table of Contents

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   Book Overview
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   Chapter 1 A Double-Loop Algorithm to Minimize the Bethe Free Energy
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   Chapter 2 A Variational Approach to Maximum a Posteriori Estimation for Image Denoising
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   Chapter 3 Maximum Likelihood Estimation of the Template of a Rigid Moving Object
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   Chapter 4 Metric Similarities Learning through Examples: An Application to Shape Retrieval
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   Chapter 5 A Fast MAP Algorithm for 3D Ultrasound
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   Chapter 6 Designing the Minimal Structure of Hidden Markov Model by Bisimulation
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   Chapter 7 Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields
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   Chapter 8 Matching Images to Models — Camera Calibration for 3-D Surface Reconstruction
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    Chapter 9 A Hierarchical Markov Random Field Model for Figure-Ground Segregation
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    Chapter 10 Articulated Object Tracking via a Genetic Algorithm
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    Chapter 11 Learning Matrix Space Image Representations
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    Chapter 12 Supervised Texture Segmentation by Maximising Conditional Likelihood
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    Chapter 13 Designing Moiré Patterns
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    Chapter 14 Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods
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    Chapter 15 Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions
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    Chapter 16 Path Based Pairwise Data Clustering with Application to Texture Segmentation
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    Chapter 17 A Maximum Likelihood Framework for Grouping and Segmentation
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    Chapter 18 Image Labeling and Grouping by Minimizing Linear Functionals over Cones
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    Chapter 19 Grouping with Directed Relationships
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    Chapter 20 Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model
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    Chapter 21 Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing
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    Chapter 22 Edge Based Probabilistic Relaxation for Sub-pixel Contour Extraction
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    Chapter 23 Two Variational Models for Multispectral Image Classification
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    Chapter 24 An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
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    Chapter 25 A Discrete/Continuous Minimization Method in Interferometric Image Processing
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    Chapter 26 Global Energy Minimization: A Transformation Approach
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    Chapter 27 Global Feedforward Neural Network Learning for Classification and Regression
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    Chapter 28 Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
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    Chapter 29 Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling
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    Chapter 30 Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems
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    Chapter 31 A Complementary Pivoting Approach to Graph Matching
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    Chapter 32 Application of Genetic Algorithms to 3-D Shape Reconstruction in an Active Stereo Vision System
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    Chapter 33 A Markov Process Using Curvature for Filtering Curve Images
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    Chapter 34 Geodesic Interpolating Splines
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    Chapter 35 Averaged Template Matching Equations
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    Chapter 36 A Continuous Shape Descriptor by Orientation Diffusion
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    Chapter 37 Multiple Contour Finding and Perceptual Grouping as a Set of Energy Minimizing Paths
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    Chapter 38 Shape Tracking Using Centroid-Based Methods
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    Chapter 39 Optical Flow and Image Registration: A New Local Rigidity Approach for Global Minimization
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    Chapter 40 Spherical Object Reconstruction Using Star-Shaped Simplex Meshes
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    Chapter 41 Gabor Feature Space Diffusion via the Minimal Weighted Area Method
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    Chapter 42 3D Flux Maximizing Flows