Издательство CRC Press, 2012, -562 pp.The last two decades have witnessed the explosive growth of image production from digital photographs to the medical scans, satellite images, and video films. Consequently, the number of applications based on digital images has drastically increased, including multimedia integration, computer animation, video games, communication and digital arts, medicine, biometry, etc. Although being very different from one another, all these application areas rely on similar image processing and analysis techniques. The field of image or video processing analysis is very broad, encompassing a wide variety of research issues from low-level processing (such as image enhancement, restoration, and segmentation) to high-level analysis (semantic object extraction, indexing databases of images, and computer-human interaction).
Recently, graphs have emerged as a unified representation for the processing and the analysis of images. The number of concepts that can be defined with graphs is very large. In particular, many real-world problems have been successfully modeled using graphs. Consequently, graph theory has found many developments and applications for image processing and analysis, particularly due to the suitability of graphs to represent any discrete data by modeling neighborhood relationships. Different graph models have been proposed for image analysis, depending on the structures to analyze.
However, graphs are not only of interest for representing the data to process, but also for defining graph-theoretical algorithms that enable the processing of functions associated with graphs. Additionally, representing problems with graphs makes it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions. This research topic is timely, very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Consequently, graphs have become indispensable for the development of cutting-edge research and applications in image processing and analysis.
With the rapid development of graphs in image processing and analysis, the book aims at providing a comprehensive overview of the current state-of-the-art. The book not only covers the theoretical aspects of image processing with graphs but also demonstrates how these concepts can be used to design cutting-edge solutions to real world applications. Due to the wide variety of problems being solved with graphs in image processing and computer vision, the book has the form a contributed volume in which each chapter addresses a specific technique or application and is written by renowned experts in the field.
The intended audience for the book is graduate and postgraduate students, researchers, and practitioners. The aim is first to provide students and researchers with a state-of-the-art view of the important ideas involved in the use of graphs in image processing and analysis. Secondly, the book provides application examples showing how the theoretical algorithms can be applied in practice. Therefore, the book can serve as a support for graduate courses in image processing and computer vision as well as a reference for practicing engineers for the development and implementation of image processing and analysis algorithms.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
