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.

Résumé
This book 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. Features: discusses similarity/dissimilarity measures, point detectors, feature extraction/selection and homogeneous/heterogeneous descriptors; examines robust estimators, point pattern matching algorithms, transformation functions, and image resampling and blending; covers principal axes methods, hierarchical methods, optimization-based methods, edge-based methods, model-based methods, and adaptive methods; includes a glossary, an extensive list of references, and an appendix on PCA.

Contenu

Introduction.- Similarity and Dissimilarity Measures.- Point Detectors.- Feature Extraction.- Image Descriptors.- Feature Selection and Heterogeneous Descriptors.- Point Pattern Matching.- Robust Parameter Estimation.- Transformation Functions.- Image Resampling and Compositing.- Image Registration Methods.- A Principal Component Analysis (PCA).