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 wider than that found in any competing book, 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. This text is especially strong and up-to-date in its treatment of 3D vision, with many topics not covered at all in competing books. It is also distinguished by its easy-to-understand algorithm descriptions of difficult concepts, and a wealth of carefully selected problems and examples that can be worked with any general-purpose image processing software package or programming environment.
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Vision allows humans to perceive and understand the world surrounding them; computer vision aims to duplicate human vision by electronically perceiving and understanding an image. Image Processing, Analysis and Machine Vision is a comprehensive introduction to the field providing up-to-date coverage of all aspects of the subject. This book reflects the authors' experience in teaching one and two semester undergraduate and graduate courses in Digital Image Processing, Digital Image Analysis, Machine Vision and Intelligent Robotics, it is also influenced by their active research in these area. Many algorithms, diagrams, examples and up-to-date references make this book required reading for students and professionals involved in computer vision.About the Author:
Milan Sonka is Professor of Electrical and Computer Engineering at the University of Iowa. His research interests include medical image analysis, computer-aided diagnosis, and machine vision.
Vaclav Hlavac is Professor of Cybernetics at the Czech Technical University, Prague. his research interests are knowledge based image analysis, 3D model-based vision and relations between statistical and structural pattern recognition.
Roger Boyle is Professor Emeritus of Computing and was Head of the School of Computing at the University of Leeds, England where his research interests are low-level vision and pattern recognition.
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