图像处理和分析的图模型：理论与应用 (Image Processing and Analysis with Graphs: Theory and Practice)

TLDR: This book discusses Graph Theory concepts and definitions used in Image Processing and Analysis, and the role of Graphs in Matching Shapes and in Categorization in GED Computation Applications of GED.

Abstract: Graph Theory Concepts and Definitions Used in Image Processing and Analysis, O. Lezoray and L. Grady Introduction Basic Graph Theory Graph Representation Paths, Trees, and Connectivity Graph Models in Image Processing and Analysis Graph Cuts-Combinatorial Optimization in Vision, H. Ishikawa Introduction Markov Random Field Basic Graph Cuts: Binary Labels Multi-Label Minimization Examples Higher-Order Models in Computer Vision, P. Kohli and C. Rother Introduction Higher-Order Random Fields Patch and Region-Based Potentials Relating Appearance Models and Region-Based Potentials Global Potentials Maximum a Posteriori Inference A Parametric Maximum Flow Approach for Discrete Total Variation Regularization, A. Chambolle and J. Darbon Introduction Idea of the approach Numerical Computations Applications Targeted Image Segmentation Using Graph Methods, L. Grady The Regularization of Targeted Image Segmentation Target Specification Conclusion A Short Tour of Mathematical Morphology on Edge and Vertex Weighted Graphs, L. Najman and F. Meyer Introduction Graphs and lattices Neighborhood Operations on Graphs Filters Connected Operators and Filtering with the Component Tree Watershed Cuts MSF Cut Hierarchy and Saliency Maps Optimization and the Power Watershed Partial Difference Equations on Graphs for Local and Nonlocal Image Processing, A. Elmoataz, O. Lezoray, V.-T. Ta, and S. Bougleux Introduction Difference Operators on Weighted Graphs Construction of Weighted Graphs p-Laplacian Regularization on Graphs Examples Image Denoising with Nonlocal Spectral Graph Wavelets, D.K. Hammond, L. Jacques, and P. Vandergheynst Introduction Spectral Graph Wavelet Transform Nonlocal Image Graph Hybrid Local/Nonlocal Image Graph Scaled Laplacian Model Applications to Image Denoising Conclusions Acknowledgments Image and Video Matting, J. Wang Introduction Graph Construction for Image Matting Solving Image Matting Graphs Data Set Video Matting Optimal Simultaneous Multisurface and Multiobject Image Segmentation, X. Wu, M.K. Garvin, and M. Sonka Introduction Motivation and Problem Description Methods for Graph-Based Image Segmentation Case Studies Conclusion Acknowledgments Hierarchical Graph Encodings, L. Brun and W. Kropatsch Introduction Regular Pyramids Irregular Pyramids Parallel construction schemes Irregular Pyramids and Image properties Graph-Based Dimensionality Reduction, J.A. Lee and M. Verleysen Summary Introduction Classical methods Nonlinearity through Graphs Graph-Based Distances Graph-Based Similarities Graph embedding Examples and comparisons Graph Edit Distance-Theory, Algorithms, and Applications, M. Ferrer and H. Bunke Introduction Definitions and Graph Matching Theoretical Aspects of GED GED Computation Applications of GED The Role of Graphs in Matching Shapes and in Categorization, B. Kimia Introduction Using Shock Graphs for Shape Matching Using Proximity Graphs for Categorization Conclusion Acknowledgment 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching, A. Sharma, R. Horaud, and D. Mateus Introduction Graph Matrices Spectral Graph Isomorphism Graph Embedding and Dimensionality Reduction Spectral Shape Matching Experiments and Results Discussion Appendix: Permutation and Doubly- stochastic Matrices Appendix: The Frobenius Norm Appendix: Spectral Properties of the Normalized Laplacian Modeling Images with Undirected Graphical Models, M.F. Tappen Introduction Background Graphical Models for Modeling Image Patches Pixel-Based Graphical Models Inference in Graphical Models Learning in Undirected Graphical Models Tree-Walk Kernels for Computer Vision, Z. Harchaoui and F. Bach Introduction Tree-Walk Kernels as Graph Kernels The Region Adjacency Graph Kernel as a Tree-Walk Kernel The Point Cloud Kernel as a Tree-Walk Kernel Experimental Results Conclusion Acknowledgments