Ebook: Applied Graph Theory in Computer Vision and Pattern Recognition
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics)
- Series: Studies in Computational Intelligence 52
- Year: 2007
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Multiresolution Image Segmentations in Graph Pyramids....Pages 3-41
A Graphical Model Framework for Image Segmentation....Pages 43-63
Digital Topologies on Graphs....Pages 65-82
Front Matter....Pages 84-84
How and Why Pattern Recognition and Computer Vision Applications Use Graphs....Pages 85-135
Efficient Algorithms on Trees and Graphs with Unique Node Labels....Pages 137-149
A Generic Graph Distance Measure Based on Multivalent Matchings....Pages 151-181
Learning from Supervised Graphs....Pages 183-201
Front Matter....Pages 204-204
Graph-Based and Structural Methods for Fingerprint Classification....Pages 205-226
Graph Sequence Visualisation and its Application to Computer Network Monitoring and Abnormal Event Detection....Pages 227-245
Clustering of Web Documents Using Graph Representations....Pages 247-265
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.
Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Multiresolution Image Segmentations in Graph Pyramids....Pages 3-41
A Graphical Model Framework for Image Segmentation....Pages 43-63
Digital Topologies on Graphs....Pages 65-82
Front Matter....Pages 84-84
How and Why Pattern Recognition and Computer Vision Applications Use Graphs....Pages 85-135
Efficient Algorithms on Trees and Graphs with Unique Node Labels....Pages 137-149
A Generic Graph Distance Measure Based on Multivalent Matchings....Pages 151-181
Learning from Supervised Graphs....Pages 183-201
Front Matter....Pages 204-204
Graph-Based and Structural Methods for Fingerprint Classification....Pages 205-226
Graph Sequence Visualisation and its Application to Computer Network Monitoring and Abnormal Event Detection....Pages 227-245
Clustering of Web Documents Using Graph Representations....Pages 247-265
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