MATHEMATICS OF DIGITAL IMAGES

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Compression, restoration and recognition are three of the key components of digital
imaging. The mathematics needed to understand and carry out all these components is here explained in a textbook that is at once rigorous and practical with many worked examples, exercises with solutions, pseudocode, and sample calculations on images. The
introduction lists fast tracks to special topics such as Principal Component Analysis,and ways into and through the book, which abounds with illustrations. The first part describes plane geometry and pattern-generating symmetries, along with some text on 3D rotation and reflection matrices. Subsequent chapters cover vectors, matrices and probability. These are applied to simulation, Bayesian methods, Shannon’s Information Theory, compression, filtering and tomography. The book will be suited for course use
or for self-study. It will appeal to all those working in biomedical imaging and diagnosis,
computer graphics, machine vision, remote sensing, image processing, and information
theory and its applications.
Dr S. G. Hoggar is a research fellow and formerly a senior lecturer in mathematics
at the University of Glasgow

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