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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.
Boxicity Год: 2011 |
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Adaptive Optics for Vision Science Автор: Jason Porter Год: 2006 |
Personal Efficiency (Classic Reprint) Автор: James Samuel Knox Год: 2010 |
Machine Vision: Theory, Algorithms, Practicalities Автор: Davies E. R. Год: 2004 |
Handbook of computer vision algorithms in image algebra Автор: Joseph N. Wilson Год: 2000 |