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

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Lecture Notes in Computer Science 2683 -- Energy Minimization Methods in Computer Vision and Pattern Recognition -- Copyright -- Preface -- Organization -- Table of Contents -- Part I Unsupervised Learning and Matching -- Part II Probabilistic Modelling -- Part III Segmentation and Grouping -- Using Prior Shape and Points in Medical Image Segmentation -- Separating a Texture from an Arbitrary Background Using Pairwise Grey Level Cooccurrences -- Part IV Shape Modelling -- Surface Recovery from 3D Point Data Using a Combined Parametric and Geometric Flow Approach -- Geometric Analysis of Continuous, Planar Shapes -- Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling* -- Definition of a Signal-to-Noise Ratio for Object Segmentation Using Polygonal MDL-Based Statistical Snakes -- Part V Restoration and Reconstruction -- Minimization of Cost-Functions with Non-smooth Data-Fidelity Terms to Clean Impulsive Noise -- A Fast GEM Algorithm for Bayesian Wavelet-Based Image Restoration Using a Class of Heavy-Tailed Priors★ -- Diffusion Tensor MR Image Restoration★ -- A MAP Estimation Algorithm Using IIR Recursive Filters★ -- Estimation of Rank Deficient Matrices from Partial Observations: Two-Step Iterative Algorithms★ -- Contextual and Non-combinatorial Approach to Feature Extraction -- Part VI Graphs and Graph-Based Methods -- Generalizing the Motzkin-Straus Theorem to Edge-Weighted Graphs, with Applications to Image Segmentation -- Generalized Multi-camera Scene Reconstruction Using Graph Cuts -- Graph Matching Using Spectral Seriation -- Author Index

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