Applied Graph Theory in Computer Vision and Pattern Recognition [electronic resource] / edited by Abraham Kandel, Horst Bunke, Mark Last.

By: Kandel, Abraham [editor.]Contributor(s): Bunke, Horst [editor.] | Last, Mark [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Studies in Computational Intelligence: 52Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007Description: X, 266 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540680208Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
Applied Graph Theory for Low Level Image Processing and Segmentation -- Multiresolution Image Segmentations in Graph Pyramids -- A Graphical Model Framework for Image Segmentation -- Digital Topologies on Graphs -- Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition -- How and Why Pattern Recognition and Computer Vision Applications Use Graphs -- Efficient Algorithms on Trees and Graphs with Unique Node Labels -- A Generic Graph Distance Measure Based on Multivalent Matchings -- Learning from Supervised Graphs -- Special Applications -- Graph-Based and Structural Methods for Fingerprint Classification -- Graph Sequence Visualisation and its Application to Computer Network Monitoring and Abnormal Event Detection -- Clustering of Web Documents Using Graph Representations.
In: Springer eBooksSummary: This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.
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Applied Graph Theory for Low Level Image Processing and Segmentation -- Multiresolution Image Segmentations in Graph Pyramids -- A Graphical Model Framework for Image Segmentation -- Digital Topologies on Graphs -- Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition -- How and Why Pattern Recognition and Computer Vision Applications Use Graphs -- Efficient Algorithms on Trees and Graphs with Unique Node Labels -- A Generic Graph Distance Measure Based on Multivalent Matchings -- Learning from Supervised Graphs -- Special Applications -- Graph-Based and Structural Methods for Fingerprint Classification -- Graph Sequence Visualisation and its Application to Computer Network Monitoring and Abnormal Event Detection -- Clustering of Web Documents Using Graph Representations.

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

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