Compression, restoration and recognition are three of the key components of digital imaging. The mathematics needed to understand and carry out all these components are explained here in a style 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 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 advanced courses 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.

'This book succeeds in its objective and if you need to understand all the intricacies of handling digital images it is about as good as you can get on the theory side.'

Source: Cvu

'I was very impresses with Mathematics of Images. the writing style, editing and figures and overall presentation are excellent. I found it a pleasure to read and will keep it handy as a reference book. this is a book that I would have found extremely useful at the start of my PhD, and I would recommend it particularly for graduate students who need a robust reference on mathematical foundations.'

Source: IAPR Newsletter

' … a useful reference tool for those wanting to understand basic image processing techniques. It is clearly written and easy to follow.'

Source: SIAM