Image registration is the process of finding correspondence between all points in
two images of a scene. This correspondence is required in stereo depth perception,
3-D scene reconstruction, object detection and recognition, change detection, image
fusion, object tracking and motion analysis. Analysis of two or more images of a
scene often depends on the ability to find correspondence between points in the
This monograph overviews principles, tools and methods in image registration.
In addition to reviewing past tools and methods and comparing their performances,
new tools and methods are introduced and evaluated.
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.