Image processing, analysis, and machine vision are an exciting and dynamic part of cognitive and computer science. Following an explosion of interest during the 1970s and 1980s, subsequent decades were characterized by a maturing of the field and significant growth of active applications; remote sensing, technical diagnostics, autonomous vehicle guidance, biomedical imaging (2D, 3D, and 4D) and automatic surveillance are the most
rapidly developing areas. This progress can be seen in an increasing number of software
and hardware products on the market—as a single example of many, the omnipresence
of consumer-level digital cameras, each of which depends on a sophisticated chain of
embedded consumer-invisible image processing steps performed in real time, is striking. Reflecting this continuing development, the number of digital image processing and machine vision courses offered at universities worldwide continues to increase rapidly.
This book reflects the authors’ experience in teaching one- and two-semester undergraduate and graduate courses in Digital Image Processing, Digital Image Analysis,
Image Understanding, Medical Imaging, Machine Vision, Pattern Recognition, and Intelligent Robotics at their respective institutions. We hope that this combined experience
will give a thorough grounding to the beginner and provide material that is advanced
enough to allow the more mature student to understand fully the relevant areas of the
subject. We acknowledge that in a very short time the more active areas will have moved
beyond this text.
This book could have been arranged in many ways. It begins with low-level processing and works its way up to higher levels of image interpretation; the authors have chosen
this framework because they believe that image understanding originates from a common database of information. The book is formally divided into 16 chapters, beginning
with low-level processing and working toward higher-level image representation, although
this structure will be less apparent after Chapter 12, when we present mathematical morphology, image compression, texture, and motion analysis which are very useful but often
special-purpose approaches that may not always be included in the processing chain.