A hands-on guide to image registration theory and methods—with examples of a wide range of real-world applications Theory and Applications of Image Registration offers comprehensive coverage of feature-based image registration methods. It provides in-depth exploration of an array of fundamental issues, including image orientation detection, similarity measures, feature extraction methods, and elastic transformation functions. Also covered are robust parameter estimation, validation methods, multi-temporal and multi-modality image registration, methods for determining the orientation of an image, methods for identifying locally unique neighborhoods in an image, methods for detecting lines in an image, methods for finding corresponding points and corresponding lines in images, registration of video images to create panoramas, and much more. Theory and Applications of Image Registration provides readers with a practical guide to the theory and underpinning principles. Throughout the book numerous real-world examples are given, illustrating how image registration can be applied to problems in various fields, including biomedicine, remote sensing, and computer vision. Also provided are software routines to help readers develop their image registration skills. Many of the algorithms described in the book have been implemented, and the software packages are made available to the readers of the book on a companion website. In addition, the book: Explores the fundamentals of image registration and provides a comprehensive look at its multi-disciplinary applications Reviews real-world applications of image registration in the fields of biomedical imaging, remote sensing, computer vision, and more Discusses methods in the registration of long videos in target tracking and 3-D reconstruction Addresses key research topics and explores potential solutions to a number of open problems in image registration Includes a companion website featuring fully implemented algorithms and image registration software for hands-on learning Theory and Applications of Image Registration is a valuable resource for researchers and professionals working in industry and government agencies where image registration techniques are routinely employed. It is also an excellent supplementary text for graduate students in computer science, electrical engineering, software engineering, and medical physics.

This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet's surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

In modern medicine, imaging is the most effective tool for diagnostics, treatment planning and therapy. Almost all modalities have went to directly digital acquisition techniques and processing of this image data have become an important option for health care in future. This book is written by a team of internationally recognized experts from all over the world. It provides a brief but complete overview on medical image processing and analysis highlighting recent advances that have been made in academics. Color figures are used extensively to illustrate the methods and help the reader to understand the complex topics.

Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

This text provides an introduction to image registration with particular emphasis on numerical methods in medical imaging. Designed for researchers in industry and academia, it should also be a suitable study guide for graduate mathematicians, computer scientists and medical physicists.

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.

Information Theory (IT) tools, widely used in many scientific fields such as engineering, physics, genetics, neuroscience, and many others, are also useful transversal tools in image processing. In this book, we present the basic concepts of IT and how they have been used in the image processing areas of registration, segmentation, video processing, and computational aesthetics. Some of the approaches presented, such as the application of mutual information to registration, are the state of the art in the field. All techniques presented in this book have been previously published in peer-reviewed conference proceedings or international journals. We have stressed here their common aspects, and presented them in an unified way, so to make clear to the reader which problems IT tools can help to solve, which specific tools to use, and how to apply them. The IT basics are presented so as to be self-contained in the book. The intended audiences are students and practitioners of image processing and related areas such as computer graphics and visualization. In addition, students and practitioners of IT will be interested in knowing about these applications.

The purpose of this book is to provide a practical introduction to the th- ries, techniques and applications of image fusion. The present work has been designed as a textbook for a one-semester ?nal-year undergraduate, or ?r- year graduate, course in image fusion. It should also be useful to practising engineers who wish to learn the concepts of image fusion and apply them to practical applications. In addition, the book may also be used as a supp- mentary text for a graduate course on topics in advanced image processing. The book complements the author’s previous work on multi-sensor data [1] fusion by concentrating exclusively on the theories, techniques and app- cations of image fusion. The book is intended to be self-contained in so far as the subject of image fusion is concerned, although some prior exposure to the ?eld of computer vision and image processing may be helpful to the reader. Apart from two preliminary chapters, the book is divided into three parts.

This book is a comprehensive treatise on the theory and applications of moment functions in image analysis. Moment functions are widely used in various realms of computer vision and image processing. Numerous algorithms and techniques have been developed using image moments, in the areas of pattern recognition, object identification, three-dimensional object pose estimation, robot sensing, image coding and reconstruction. This book provides a compilation of the theoretical aspects related to different types of moment functions, and their applications in the above areas.The book is organized into two parts. The first part discusses the fundamental concepts behind important moments such as geometric moments, complex moments, Legendre moments, Zernike moments, and moment tensors. Most of the commonly used properties of moment functions and the mathematical framework for the derivation of basic theorems and results are discussed in detail. This includes the derivation of moment invariants, implementation aspects of moments, transform properties, and fast methods for computing the moment functions for both binary and gray-level images. The second part presents the key application areas of moments such as pattern recognition, object identification, image-based pose estimation, edge detection, clustering, segmentation, coding and reconstruction. Important algorithms in each of these areas are discussed. A comprehensive list of bibliographical references on image moments is also included.