Digital imaging is now ubiquitous, from the smart phone in your pocket to state-of-the-art medical imaging and satellite imagery. Raw imaging data can be manipulated using computer algorithms to perform a range of functions from reducing noise to complex image analysis and pattern and object recognition. The result is that digital image processing has applications in almost all areas of science and engineering.Designed for a one semester course, the aim of this book is to concentrate on the principles and techniques of image processing. It provides an in-depth presentation of key topics, including many techniques not included in introductory texts. Practical implementation of the various image processing algorithms is an important step in learning the subject, and computer packages such as MATLAB (R) facilitate this without the need to learn more complex programming languages.

The Second edition includes new problems and programming exercises to all chapters, and chapters have been updated as necessary. Sample programmes are again provided as supplementary material accessible from within the ebook or the book's web page.

A few new sections and more importantly two new chapters have been added, extending the coverage and usability of the book. Whilst the first edition has a chapter on Image analysis and object recognition, many important topics such as artificial neural networks are not discussed. These topics are of significant current interest and are important for both students and professionals who wish to learn about this area. Accordingly a new chapter on machine learning has been added, where many techniques of machine learning including artificial neural networks are discussed. The other new chapter concentrates on error correcting codes and cryptography.