- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Preface
- The Editors
- Contributors
- Table of Contents
- 1 Graph theory concepts and definitions used in image processing and analysis
- 2 Graph Cuts—Combinatorial Optimization in Vision
- 3 Higher-Order Models in Computer Vision
- 4 A Parametric Maximum Flow Approach for Discrete Total Variation Regularization
- 5 Targeted Image Segmentation Using Graph Methods
- 6 A Short Tour of Mathematical Morphology on Edge and Vertex Weighted Graphs
- 7 Partial difference Equations on Graphs for Local and Nonlocal Image Processing
- 8 Image Denoising with Nonlocal Spectral Graph Wavelets
- 9 Image and Video Matting
- 10 Optimal Simultaneous Multisurface and Multiobject Image Segmentation
- 11 Hierarchical Graph Encodings
- 12 Graph-Based Dimensionality Reduction
- 13 Graph Edit Distance—Theory, Algorithms, and Applications
- 14 The Role of Graphs in Matching Shapes and in Categorization
- 15 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
- 15.1 Introduction
- 15.2 Graph Matrices
- 15.3 Spectral Graph Isomorphism
- 15.4 Graph Embedding and Dimensionality Reduction
- 15.5 Spectral Shape Matching
- 15.6 Experiments and Results
- 15.7 Discussion
- 15.8 Appendix: Permutation and Doubly- stochastic Matrices
- 15.9 Appendix: The Frobenius Norm
- 15.10 Appendix: Spectral Properties of the Normalized Laplacian
- Bibliography
- 16 Modeling Images with Undirected Graphical Models
- 17 Tree-Walk Kernels for Computer Vision
- Index
Dedication
To my beloved wife Sophie, daughter Manon, and son Quentin.
Their love and patience are an important support.
Olivier Lézoray
To my very patient, understanding and loving wife Amy,
my daughter Alexandra and son Leo.
Leo Grady
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