Image Processing and Analysis with Graphs (eBook, ePUB)

Theory and Practice
Redaktion: Lezoray, Olivier; Grady, Leo

• Format: ePub

Produktbeschreibung

The first book to serve as a comprehensive review of digital imaging and computer vision with graphs, this reference helps readers unfamiliar with presented concepts. The book is divided into two parts that focus on the processing of functions on graphs, graph-based image processing, representation and analysis of objects on graphs, and graph-based image analysis. Each chapter provides a comprehensive review on a specific topic. These range from research challenges to industry trends, and contributors provide numerous examples to illustrate how the proposed methods can be used in practice. A companion website is available.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 570
• Erscheinungstermin: 12. Juli 2017
• Englisch
• ISBN-13: 9781351833172
• Artikelnr.: 48868080

Autorenporträt

Olivier Lézoray received his B.Sc. in mathematics and computer science, as well as his M.Sc. and Ph.D. degrees from the Department of Computer Science, University of Caen, France, in 1992, 1996, and 2000, respectively. From September 1999 to August 2000, he was an assistant professor with the Department of Computer Science at the University of Caen. From September 2000 to August 2009, he was an associate professor at the Cherbourg Institute of Technology of the University of Caen, in the Communication Networks and Services Department. In July 2008, he was a visiting research fellow at the University of Sydney, Australia. Since September 2009, he has been a full professor at the Cherbourg Institute of Technology of the University of Caen, in the Communication Networks and Services Department. He also serves as Chair of the Institute Research Committee. In 2011 he cofounded Datexim and is a member of the scientific board of the company, which brought state-of-art image and data processing to market with applications in digital pathology. His research focuses on discrete models on graphs for image processing and analysis, image data classification by machine learning, and computer-aided diagnosis. Leo Grady received his B.Sc. degree in electrical engineering from the University of Vermont in 1999 and a Ph.D. degree from the Cognitive and Neural Systems Department at Boston University in 2003. Dr. Grady was with Siemens Corporate Research in Princeton, where he worked as a Principal Research Scientist in the Image Analytics and Informatics division. He recently left Siemens to become Vice President of R&D at HeartFlow. The focus of his research has been on the modeling of images and other data with graphs. These graph models have generated the development and application of tools from discrete calculus, combinatorial/continuous optimization, and network analytics to perform analysis and synthesis of the images/data. The primary applications of his work have been in computer vision and biomedical applications. Dr. Grady currently holds 30 granted patents with more than 40 additional patents currently under review. He has also contributed to more than 20 Siemens products that target biomedical applications and are used in medical centers worldwide.

Inhaltsangabe

Graph Theory Concepts and Definitions Used in Image Processing and
Analysis. Graph Cuts-Combinatorial Optimization in Vision. Higher-Order
Models in Computer Vision. A Parametric Maximum Flow Approach for Discrete
Total Variation Regularization. Targeted Image Segmentation Using Graph
Methods. A Short Tour of Mathematical Morphology on Edge and Vertex
Weighted Graphs. Partial Difference Equations on Graphs for Local and
Nonlocal Image Processing. Image Denoising with Nonlocal Spectral Graph
Wavelets. Image and Video Matting. Optimal Simultaneous Multisurface and
Multiobject Image Segmentation. Hierarchical Graph Encodings. Graph-Based
Dimensionality Reduction. Graph Edit Distance-Theory, Algorithms, and
Applications. The Role of Graphs in Matching Shapes and in Categorization.
3D Shape Registration Using Spectral Graph Embedding and Probabilistic
Matching. Modeling Images with Undirected Graphical Models. Tree-Walk
Kernels for Computer Vision.