Applied Graph Theory in Computer Vision and Pattern Recognition

Kandel, Abraham and Horste Bunke \(editors\)

ISBN 10: 3540680195 / ISBN 13: 9783540680192

Verlag: Springer, 2007

Beschreibung

Springer, 2007. 1st Edition . As New/No Jacket. Book still in original shrink-wrap.

Bestandsnummer des Verkäufers JP14350

Bibliografische Details

• Titel: Applied Graph Theory in Computer Vision and Pattern Recognition
• Verlag: Springer
• Veröffentlichungsdatum: 2007
• Sprache: Englisch
• ISBN-10: 3540680195
• ISBN-13: 9783540680192
• Bindung: Book Hardcover
• Ausgabe: 1st Edition.
• Zustand: As New
• Zustand des Schutzumschlags: No Jacket
• Verkäuferkataloge:
  • Computer Science

Über diesen Titel

Inhaltsangabe

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.

Von der hinteren Coverseite

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.