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Image Processing and Analysis with Graphs: Digital Imaging and Computer Vision, cartea 5

Autor Olivier Lezoray, Leo Grady Editat de Olivier Lezoray
en Limba Engleză Hardback – 10 iul 2012
Covering the theoretical aspects of image processing and analysis through the use of graphs in the representation and analysis of objects, Image Processing and Analysis with Graphs: Theory and Practice also demonstrates how these concepts are indispensible for the design of cutting-edge solutions for real-world applications.
Explores new applications in computational photography, image and video processing, computer graphics, recognition, medical and biomedical imaging
With the explosive growth in image production, in everything from digital photographs to medical scans, there has been a drastic increase in the number of applications based on digital images. This book explores how graphs—which are suitable to represent any discrete data by modeling neighborhood relationships—have emerged as the perfect unified tool to represent, process, and analyze images. It also explains why graphs are ideal for defining graph-theoretical algorithms that enable the processing of functions, making it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions.
Some key subjects covered in the book include:
  • Definition of graph-theoretical algorithms that enable denoising and image enhancement
  • Energy minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields
  • Image processing with graphs: targeted segmentation, partial differential equations, mathematical morphology, and wavelets
  • Analysis of the similarity between objects with graph matching
  • Adaptation and use of graph-theoretical algorithms for specific imaging applications in computational photography, computer vision, and medical and biomedical imaging
Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision, this book is a contributed volume of chapters written by renowned experts who address specific techniques or applications. This state-of-the-art overview provides application examples that illustrate practical application of theoretical algorithms. Useful as a support for graduate courses in image processing and computer vision, it is also perfect as a reference for practicing engineers working on development and implementation of image processing and analysis algorithms.
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Specificații

ISBN-13: 9781439855072
ISBN-10: 1439855072
Pagini: 537
Ilustrații: 169 black & white illustrations, 16 colour illustrations, 12 black & white tables
Dimensiuni: 157 x 231 x 36 mm
Greutate: 0.96 kg
Ediția:New.
Editura: CRC Press
Seria Digital Imaging and Computer Vision


Public țintă

Academia, industrial engineers, managers, researchers, and designers.

Cuprins

Graph Theory Concepts and Definitions Used in Image Processing and Analysis, O. Lezoray and L. Grady
Introduction
Basic Graph Theory
Graph Representation
Paths, Trees, and Connectivity
Graph Models in Image Processing and Analysis

Graph Cuts—Combinatorial Optimization in Vision,
H. Ishikawa
Introduction
Markov Random Field
Basic Graph Cuts: Binary Labels
Multi-Label Minimization
Examples

Higher-Order Models in Computer Vision,
P. Kohli and C. Rother
Introduction
Higher-Order Random Fields
Patch and Region-Based Potentials
Relating Appearance Models and Region-Based Potentials
Global Potentials
Maximum a Posteriori Inference

A Parametric Maximum Flow Approach for Discrete Total Variation Regularization,
A. Chambolle and J. Darbon
Introduction
Idea of the approach
Numerical Computations
Applications

Targeted Image Segmentation Using Graph Methods,
L. Grady
The Regularization of Targeted Image Segmentation
Target Specification
Conclusion

A Short Tour of Mathematical Morphology on Edge and Vertex Weighted Graphs,
L. Najman and F. Meyer
Introduction
Graphs and lattices
Neighborhood Operations on Graphs
Filters
Connected Operators and Filtering with the Component Tree
Watershed Cuts
MSF Cut Hierarchy and Saliency Maps
Optimization and the Power Watershed

Partial Difference Equations on Graphs for Local and Nonlocal Image Processing,
A. Elmoataz, O. Lezoray, V.-T. Ta, and S. Bougleux
Introduction
Difference Operators on Weighted Graphs
Construction of Weighted Graphs
p-Laplacian Regularization on Graphs
Examples

Image Denoising with Nonlocal Spectral Graph Wavelets,
D.K. Hammond, L. Jacques, and P. Vandergheynst
Introduction
Spectral Graph Wavelet Transform
Nonlocal Image Graph
Hybrid Local/Nonlocal Image Graph
Scaled Laplacian Model
Applications to Image Denoising
Conclusions
Acknowledgments

Image and Video Matting,
J. Wang
Introduction
Graph Construction for Image Matting
Solving Image Matting Graphs
Data Set
Video Matting

Optimal Simultaneous Multisurface and Multiobject Image Segmentation
, X. Wu, M.K. Garvin, and M. Sonka
Introduction
Motivation and Problem Description
Methods for Graph-Based Image Segmentation
Case Studies
Conclusion
Acknowledgments

Hierarchical Graph Encodings
, L. Brun and W. Kropatsch
Introduction
Regular Pyramids
Irregular Pyramids Parallel construction schemes
Irregular Pyramids and Image properties

Graph-Based Dimensionality Reduction,
J.A. Lee and M. Verleysen
Summary
Introduction
Classical methods
Nonlinearity through Graphs
Graph-Based Distances
Graph-Based Similarities
Graph embedding
Examples and comparisons

Graph Edit Distance—Theory, Algorithms, and Applications,
M. Ferrer and H. Bunke
Introduction
Definitions and Graph Matching
Theoretical Aspects of GED
GED Computation
Applications of GED

The Role of Graphs in Matching Shapes and in Categorization,
B. Kimia
Introduction
Using Shock Graphs for Shape Matching
Using Proximity Graphs for Categorization
Conclusion
Acknowledgment

3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
, A. Sharma, R. Horaud, and D. Mateus
Introduction
Graph Matrices
Spectral Graph Isomorphism
Graph Embedding and Dimensionality Reduction
Spectral Shape Matching
Experiments and Results
Discussion
Appendix: Permutation and Doubly- stochastic Matrices
Appendix: The Frobenius Norm
Appendix: Spectral Properties of the Normalized Laplacian

Modeling Images with Undirected Graphical Models
, M.F. Tappen
Introduction
Background
Graphical Models for Modeling Image Patches
Pixel-Based Graphical Models
Inference in Graphical Models
Learning in Undirected Graphical Models

Tree-Walk Kernels for Computer Vision,
Z. Harchaoui and F. Bach
Introduction
Tree-Walk Kernels as Graph Kernels
The Region Adjacency Graph Kernel as a Tree-Walk Kernel
The Point Cloud Kernel as a Tree-Walk Kernel
Experimental Results
Conclusion
Acknowledgments

Notă biografică

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.