Applied graph theory in computer vision and pattern recognition

Horst Bunke (Editor), Abraham Kandel (Editor), Mark Last

Research output: Book/ReportBook


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.
Original languageEnglish
Place of PublicationBerlin, Germany; New York, United States
Number of pages264
Edition1st ed. 2007
ISBN (Electronic)1280853050, 3540680209, 9786610853052
StatePublished - 2007

Publication series

NameStudies in Computational Intelligence


Dive into the research topics of 'Applied graph theory in computer vision and pattern recognition'. Together they form a unique fingerprint.

Cite this