Muutke küpsiste eelistusi

Image Processing and Analysis with Graphs: Theory and Practice [Pehme köide]

4.00/5 (1 hinnangut Goodreads-ist)
Edited by (Siemens Corporate Research, Princeton, New Jersey, USA), Edited by (University of Caen, France)
  • Formaat: Paperback / softback, 570 pages, kõrgus x laius: 234x156 mm, kaal: 1070 g, 12 Tables, black and white; 16 Illustrations, color; 169 Illustrations, black and white
  • Sari: Digital Imaging and Computer Vision
  • Ilmumisaeg: 29-Mar-2017
  • Kirjastus: CRC Press
  • ISBN-10: 1138071765
  • ISBN-13: 9781138071766
Teised raamatud teemal:
Image Processing and Analysis with Graphs: Theory and PracticeSuurem pilt
  • Formaat: Paperback / softback, 570 pages, kõrgus x laius: 234x156 mm, kaal: 1070 g, 12 Tables, black and white; 16 Illustrations, color; 169 Illustrations, black and white
  • Sari: Digital Imaging and Computer Vision
  • Ilmumisaeg: 29-Mar-2017
  • Kirjastus: CRC Press
  • ISBN-10: 1138071765
  • ISBN-13: 9781138071766
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138071766.html
Märksõnad:
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 graphswhich are suitable to represent any discrete data by modeling neighborhood relationshipshave 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.
1 Graph theory concepts and definitions used in image processing and analysis
1(24)
Olivier Lezoray
Leo Grady
1.1 Introduction
2(1)
1.2 Basic Graph Theory
2(3)
1.3 Graph Representation
5(2)
1.4 Paths, Trees, and Connectivity
7(8)
1.5 Graph Models in Image Processing and Analysis
15(6)
1.6 Conclusion
21(4)
Bibliography
21(4)
2 Graph Cuts---Combinatorial Optimization in Vision
25(40)
Hiroshi Ishikawa
2.1 Introduction
26(1)
2.2 Markov Random Field
27(8)
2.3 Basic Graph Cuts: Binary Labels
35(10)
2.4 Multi-Label Minimization
45(10)
2.5 Examples
55(1)
2.6 Conclusion
56(9)
Bibliography
57(8)
3 Higher-Order Models in Computer Vision
65(28)
Pushmeet Kohli
Carsten Rother
3.1 Introduction
65(2)
3.2 Higher-Order Random Fields
67(2)
3.3 Patch and Region-Based Potentials
69(8)
3.4 Relating Appearance Models and Region-Based Potentials
77(2)
3.5 Global Potentials
79(5)
3.6 Maximum a Posteriori Inference
84(5)
3.7 Conclusions and Discussion
89(4)
Bibliography
90(3)
4 A Parametric Maximum Flow Approach for Discrete Total Variation Regularization
93(18)
Antonin Chambolle
Jerome Darbon
4.1 Introduction
93(2)
4.2 Idea of the approach
95(2)
4.3 Numerical Computations
97(7)
4.4 Applications
104(7)
Bibliography
106(5)
5 Targeted Image Segmentation Using Graph Methods
111(30)
Leo Grady
5.1 The Regularization of Targeted Image Segmentation
113(5)
5.2 Target Specification
118(16)
5.3 Conclusion
134(7)
Bibliography
135(6)
6 A Short Tour of Mathematical Morphology on Edge and Vertex Weighted Graphs
141(34)
Laurent Najman
Fernand Meyer
6.1 Introduction
142(1)
6.2 Graphs and lattices
143(2)
6.3 Neighborhood Operations on Graphs
145(4)
6.4 Filters
149(3)
6.5 Connected Operators and Filtering with the Component Tree
152(2)
6.6 Watershed Cuts
154(7)
6.7 MSF Cut Hierachy and Saliency Maps
161(3)
6.8 Optimization and the Power Watershed
164(5)
6.9 Conclusion
169(6)
Bibliography
169(6)
7 Partial difference Equations on Graphs for Local and Nonlocal Image Processing
175(32)
Abderrahim Elmoataz
Olivier Lezoray
Vinh-Thong Ta
Sebastien Bougleux
7.1 Introduction
176(1)
7.2 Difference Operators on Weighted Graphs
177(5)
7.3 Construction of Weighted Graphs
182(3)
7.4 p-Laplacian Regularization on Graphs
185(7)
7.5 Examples
192(11)
7.6 Concluding Remarks
203(4)
Bibliography
203(4)
8 Image Denoising with Nonlocal Spectral Graph Wavelets
207(30)
David K. Hammond
Laurent Jacques
Pierre Vandergheynst
8.1 Introduction
208(2)
8.2 Spectral Graph Wavelet Transform
210(6)
8.3 Nonlocal Image Graph
216(4)
8.4 Hybrid Local/Nonlocal Image Graph
220(5)
8.5 Scaled Laplacian Model
225(2)
8.6 Applications to Image Denoising
227(6)
8.7 Conclusions
233(1)
8.8 Acknowledgments
234(3)
Bibliography
234(3)
9 Image and Video Matting
237(28)
Jue Wang
9.1 Introduction
237(4)
9.2 Graph Construction for Image Matting
241(9)
9.3 Solving Image Matting Graphs
250(3)
9.4 Data Set
253(1)
9.5 Video Matting
254(6)
9.6 Conclusion
260(5)
Bibliography
261(4)
10 Optimal Simultaneous Multisurface and Multiobject Image Segmentation
265(40)
Xiaodong Wu
Mona K. Garvin
Milan Sonka
10.1 Introduction
266(1)
10.2 Motivation and Problem Description
267(1)
10.3 Methods for Graph-Based Image Segmentation
268(22)
10.4 Case Studies
290(9)
10.5 Conclusion
299(1)
10.6 Acknowledgments
299(6)
Bibliography
299(6)
11 Hierarchical Graph Encodings
305(46)
Luc Brun
Walter Kropatsch
11.1 Introduction
306(1)
11.2 Regular Pyramids
307(3)
11.3 Irregular Pyramids Parallel construction schemes
310(14)
11.4 Irregular Pyramids and Image properties
324(20)
11.5 Conclusion
344(7)
Bibliography
347(4)
12 Graph-Based Dimensionality Reduction
351(32)
John A. Lee
Michel Verleysen
12.1 Summary
352(1)
12.2 Introduction
352(1)
12.3 Classical methods
353(4)
12.4 Nonlinearity through Graphs
357(1)
12.5 Graph-Based Distances
358(3)
12.6 Graph-Based Similarities
361(4)
12.7 Graph embedding
365(4)
12.8 Examples and comparisons
369(4)
12.9 Conclusions
373(10)
Bibliography
374(9)
13 Graph Edit Distance---Theory, Algorithms, and Applications
383(40)
Miquel Ferrer
Horst Bunke
13.1 Introduction
384(2)
13.2 Definitions and Graph Matching
386(10)
13.3 Theoretical Aspects of GED
396(5)
13.4 GED Computation
401(6)
13.5 Applications of GED
407(10)
13.6 Conclusions
417(6)
Bibliography
417(6)
14 The Role of Graphs in Matching Shapes and in Categorization
423(18)
Benjamin Kimia
14.1 Introduction
423(3)
14.2 Using Shock Graphs for Shape Matching
426(3)
14.3 Using Proximity Graphs for Categorization
429(8)
14.4 Conclusion
437(1)
14.5 Acknowledgment
437(4)
Bibliography
437(4)
15 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
441(30)
Avinash Sharma
Radu Horaud
Diana Mateus
15.1 Introduction
442(2)
15.2 Graph Matrices
444(2)
15.3 Spectral Graph Isomorphism
446(6)
15.4 Graph Embedding and Dimensionality Reduction
452(6)
15.5 Spectral Shape Matching
458(6)
15.6 Experiments and Results
464(4)
15.7 Discussion
468(1)
15.8 Appendix: Permutation and Doubly-stochastic Matrices
469(1)
15.9 Appendix: The Frobenius Norm
470(1)
15 10 Appendix: Spectral Properties of the Normalized Laplacian
471(4)
Bibliography
472(3)
16 Modeling Images with Undirected Graphical Models
475(24)
Marshall F. Tappen
16.1 Introduction
476(1)
16.2 Background
476(6)
16.3 Graphical Models for Modeling Image Patches
482(1)
16.4 Pixel-Based Graphical Models
483(7)
16.5 Inference in Graphical Models
490(2)
16.6 Learning in Undirected Graphical Models
492(4)
16.7 Conclusion
496(3)
Bibliography
496(3)
17 Tree-Walk Kernels for Computer Vision
499(30)
Zaid Harchaoui
Francis Bach
17.1 Introduction
500(2)
17.2 Tree-Walk Kernels as Graph Kernels
502(4)
17.3 The Region Adjacency Graph Kernel as a Tree-Walk Kernel
506(4)
17.4 The Point Cloud Kernel as a Tree-Walk Kernel
510(8)
17.5 Experimental Results
518(7)
17.6 Conlusion
525(1)
17.7 Acknowledgments
525(4)
Bibliography
525(4)
Index 529
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.