Image Processing and Analysis：Variational, PDE, Wavelet, and Stochastic Methods

Chan (mathematics, University of California) and Shen (mathematics, University of Minnesota) develop the mathematical foundations of modern image processing and low-level computer vision. They first investigate the geometric, functional, and atomic structures of images, then analyze the four most powerful classes of mathematical tools in contemporary image analysis and processing. Theoretical analysis is followed by computational implementation and numerical examples. The book is for graduate students and researchers in applied mathematics, computer science, electrical engineering, and other disciplines concerned with imaging and computer vision. Annotation c2005 Book News, Inc., Portland, OR (booknews.com)

Tony F. Chan is Professor of Mathematics and currently also Dean of the Division of Physical Sciences at the University of California, Los Angeles. His research interests include mathematical and computational methods in image processing and computer vision, brain mapping, and VLSI physical design. URL: www.math.ucla.edu/~imagers.Jianhong (Jackie) Shen is Assistant Professor of Mathematics at the University of Minnesota. In addition to doing extensive research in imaging and vision sciences, he is interested in multiscale structures and patterns in scientific data analysis as well as modeling, analysis, and computation in biological and medical sciences. URL: www.math.umn.edu/~jhshen.