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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 Stochastic Search for Optimal Linear Representations of Images on Spaces with Orthogonality Constraints
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    Chapter 2 Local PCA for Strip Line Detection and Thinning
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    Chapter 3 Curve Matching Using the Fast Marching Method
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    Chapter 4 EM Algorithm for Clustering an Ensemble of Graphs with Comb Matching
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    Chapter 5 Information Force Clustering Using Directed Trees
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    Chapter 6 Watershed-Based Unsupervised Clustering
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    Chapter 7 Active Sampling Strategies for Multihypothesis Testing
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    Chapter 8 Likelihood Based Hierarchical Clustering and Network Topology Identification
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    Chapter 9 Learning Mixtures of Tree-Unions by Minimizing Description Length
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    Chapter 10 Image Registration and Segmentation by Maximizing the Jensen-Rényi Divergence
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    Chapter 11 Asymptotic Characterization of Log-Likelihood Maximization Based Algorithms and Applications
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    Chapter 12 Maximum Entropy Models for Skin Detection
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    Chapter 13 Hierarchical Annealing for Random Image Synthesis
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    Chapter 14 On Solutions to Multivariate Maximum α -Entropy Problems
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    Chapter 15 Semi-supervised Image Segmentation by Parametric Distributional Clustering
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    Chapter 16 Path Variation and Image Segmentation
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    Chapter 17 A Fast Snake Segmentation Method Applied to Histopathological Sections
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    Chapter 18 A Compositionality Architecture for Perceptual Feature Grouping
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    Chapter 19 Using Prior Shape and Points in Medical Image Segmentation
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    Chapter 20 Separating a Texture from an Arbitrary Background Using Pairwise Grey Level Cooccurrences
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    Chapter 21 Surface Recovery from 3D Point Data Using a Combined Parametric and Geometric Flow Approach
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    Chapter 22 Geometric Analysis of Continuous, Planar Shapes
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    Chapter 23 Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling
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    Chapter 24 Definition of a Signal-to-Noise Ratio for Object Segmentation Using Polygonal MDL-Based Statistical Snakes
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    Chapter 25 Minimization of Cost-Functions with Non-smooth Data-Fidelity Terms to Clean Impulsive Noise
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    Chapter 26 A Fast GEM Algorithm for Bayesian Wavelet-Based Image Restoration Using a Class of Heavy-Tailed Priors
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    Chapter 27 Diffusion Tensor MR Image Restoration
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    Chapter 28 A MAP Estimation Algorithm Using IIR Recursive Filters
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    Chapter 29 Estimation of Rank Deficient Matrices from Partial Observations: Two-Step Iterative Algorithms
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    Chapter 30 Contextual and Non-combinatorial Approach to Feature Extraction
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    Chapter 31 Generalizing the Motzkin-Straus Theorem to Edge-Weighted Graphs, with Applications to Image Segmentation
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    Chapter 32 Generalized Multi-camera Scene Reconstruction Using Graph Cuts
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    Chapter 33 Graph Matching Using Spectral Seriation
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Energy Minimization Methods in Computer Vision and Pattern Recognition
Published by
Springer, Berlin, Heidelberg, January 2003
DOI 10.1007/b11710
978-3-54-040498-9, 978-3-54-045063-4

Anand Rangarajan, Mário Figueiredo, Josiane Zerubia

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 38%
Sweden 2 25%
Netherlands 1 13%
Switzerland 1 13%
Brazil 1 13%
Germany 1 13%
Japan 1 13%
Portugal 1 13%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 300%
Researcher 10 125%
Student > Master 8 100%
Student > Bachelor 6 75%
Student > Doctoral Student 5 63%
Other 15 188%
Readers by discipline Count As %
Computer Science 30 375%
Engineering 19 238%
Mathematics 6 75%
Physics and Astronomy 2 25%
Business, Management and Accounting 2 25%
Other 5 63%