Applied Graph Theory in Computer Vision and Pattern Recognition

ISBN 10: 3540680195 / ISBN 13: 9783540680192

Editore: Springer, 2007

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pp. 276.

Codice articolo 26478842

Dati bibliografici

• Titolo: Applied Graph Theory in Computer Vision and Pattern Recognition
• Editore: Springer
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 3540680195
• ISBN 13: 9783540680192
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.

Dalla quarta di copertina

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.