Graph-Based Representations in Pattern Recognition - Info and Reading Options

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Intro -- Title Page -- Preface -- Organization -- Table of Contents -- Graph-Based Representation and Recognition -- Matching Hierarchies of Deformable Shapes -- Introduction -- Our Contributions and Relationships to PriorWork -- Hierarchical Shape Representation -- Shape Matching -- Definition of the Objective Function of Matching -- Convexification of the Objective Function of Matching -- Results -- Conclusion -- References -- Edition within a Graph Kernel Framework for Shape Recognition -- Introduction -- Bag of Path Kernel -- Change Detection Kernel -- Path Kernel -- Skeleton-Based Graph -- Graph Representations -- Attributes -- Hierarchical Kernels -- Elementary Operations on Path -- Edition Cost -- Edition Path Kernel -- Experiments -- Conclusion -- References -- Coarse-to-Fine Matching of Shapes Using Disconnected Skeletons by Learning Class-Specific Boundary Deformations -- Introduction -- Obtaining Radius Functions -- Learning Boundary Deformations in a Shape Category -- A Coarse-to-Fine Strategy to Incorporate Similarity of Boundary Details into Category-Influenced Matching -- Experimental Results -- Summary and Conclusion -- References -- An Optimisation-Based Approach to Mesh Smoothing: Reformulation and Extensions -- Analysis and Extension of the Mesh Smoothing Problem -- Proposed Functions and Optimisation Schemes -- Fitting Other Published Algorithms within Our Framework -- Numerical Results -- References -- Graph-Based Representation of Symbolic Musical Data -- Introduction -- Graph-Based Representation of Symbolic Musical Data -- Experiments -- Summary and Outlook -- References -- Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods -- Introduction -- Preliminaries -- The Hybrid Parent and Children Algorithm -- Inter-IAPC Algorithm -- HPC Algorithm -- Experiments

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