Statistical Image Processing and Multidimensional Modeling
Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical ima
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Information Science and Statistics
SSeries Editors: M. Jordan Robert Nowak Bernhard Schölkopf
For other titles published in this series, go to www.springer.com/series/3816
Paul Fieguth
Statistical Image Processing and Multidimensional Modeling
Paul Fieguth Department of Systems Design Engineering Faculty of Engineering University of Waterloo Waterloo Ontario N2L-3G1 Canada [email protected]
ISSN 1613-9011 e-ISBN 978-1-4419-7294-1 ISBN 978-1-4419-7293-4 DOI 10.1007/978-1-4419-7294-1 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010938436 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
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Preface
As a young professor in 1997 I taught my graduate course in Stochastic Image Processing for the first time. Looking back on my rough notes from that time, the course must have been a near impenetrable disaster for the graduate students enrolled, with a long list of errors, confusions, and bad notation. With every repetition the course improved, with significant changes to notation, content, and flow. However, at the same time that a cohesive, large-scale form of the course took shape, the absence of any textbook covering this material became increasingly apparent. There are countless texts on the subjects of image processing, Kalman filtering, and signal processing, however precious little for random fields or spatial statistics. The few texts that do cover Gibbs models or Markov random fields tend to be highly mathematical research monographs, not well suited as a textbook for a graduate course. More than just a graduate course textbook, this text was developed with the goal of being a useful reference for graduate students working in the areas of image processing, spatial statistics, and random fields. In particular, there are many concepts which are known and documented in the research literature, which are useful for students to understand, but which do not appear in many textbooks. This perception is driven by my own experience as a PhD student, which would have been considerably simplified if I had had a text accessible to me addressing some of the following gaps: • FFT-based estimation (Section 8.3) • A nice, simple, clear description of multigrid (Section 9.2.
