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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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Title
Energy Minimization Methods in Computer Vision and Pattern Recognition
Published by
Springer, Berlin, Heidelberg, January 2003
DOI 10.1007/b11710
ISBNs
978-3-54-040498-9, 978-3-54-045063-4
Editors

Anand Rangarajan, Mário Figueiredo, Josiane Zerubia

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Sweden 2 2%
Germany 1 1%
Switzerland 1 1%
Brazil 1 1%
Portugal 1 1%
Japan 1 1%
Netherlands 1 1%
Unknown 70 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 32%
Researcher 10 12%
Student > Master 10 12%
Student > Bachelor 7 9%
Professor 5 6%
Other 14 17%
Unknown 9 11%
Readers by discipline Count As %
Computer Science 33 41%
Engineering 20 25%
Mathematics 6 7%
Agricultural and Biological Sciences 3 4%
Business, Management and Accounting 2 2%
Other 5 6%
Unknown 12 15%