Springer Image Registration: Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition)

This book presents a detailed guide to image registration. It details the principles behind a vast array of tools and methods as well as compares their performances using synthetic and real data. Review From the reviews: “The book Image Registration: Principles, Tools and Methods by A. Ardeshir Goshtasby is a detailed reference on … low-level computer vision sub-tasks. … The book provides explicit near-pseudo-code descriptions of some of the algorithms discussed which can be helpful for someone interested in implementing the algorithms. It has a total of 441 pages and is self-contained and easy to read, thus well suited as a reference book for students and practitioners … .” (Zeeshan Zia, IAPR Newsletter, Vol. 35 (2), April, 2013) “This book aims to describe principles, tools and methods in image registration. … New tools and methods are introduced and evaluated. The book covers the fundamentals of digital image registration … . Each chapter concludes with an extensive and … updated list of bibliographic references. This book is a valuable text for students, image analysis software developers, engineers, and researchers who would like to analyze two or more images of a scene.” (Oscar Bustos, Zentralblatt MATH, Vol. 1243, 2012) From the Back Cover Image registration is the process of finding correspondence between all points in two images of a scene – a process with numerous applications in computer vision and image analysis. This comprehensive text/reference presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and methods. The book begins by identifying the components of a general image registration system, and then describes the design of each component using various image analysis tools. The text reviews a vast array of tools and methods, not only describing the principles behind each tool and method, but also measuring and comparing their performances using synthetic and real data. Topics and features: Discusses a broad range of image analysis tools, including similarity/dissimilarity measures, point detectors, feature extraction and homogeneous descriptors, and feature selection and heterogeneous descriptors Examines robust estimators, point pattern matching algorithms, transformation functions, and image resampling and blending Covers a large number of image registration methods, such as principal axes methods, hierarchical methods, optimization-based methods, edge-based methods, model-based methods, and adaptive methods Provides all images and data used in the book at the website http://www.imgfsr.com/book2.html, enabling the reader to reproduce the results Includes a glossary, an extensive list of references, and an appendix on principal component analysis An excellent reference for courses on image registration, image processing, computer vision, pattern recognition, and image analysis, this unique text/guide is also suitable for image analysis software developers, engineers, and researchers interested in analyzing two or more images of a scene.