This robust text provides deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision. As a result, it can serve undergraduates, graduates, researchers, and professionals looking for a readable reference. The book's encyclopedic coverage of topics is wide, and it can be used in more than one course (both image processing and machine vision classes). In addition, while advanced mathematics is not needed to understand basic concepts (making this a good choice for undergraduates), rigorous mathematical coverage is included for more advanced readers. It is also distinguished by its easy-to-understand algorithm descriptions of difficult concepts, and a wealth of carefully selected problems and examples.
This textbook can be used for an undergraduate course in digital image processing and analysis, or an undergraduate/graduate course in computer vision. The updated version presents new sections on compression via JPEG and MPEG, fractals, fuzzy logic recognition, hidden Markov models, Kalman filters, point distribution models, 3D vision, watershed segmentation, wavelets, and case studies. Annotation c. by Book News, Inc., Portland, Or.