||This book is intended as an introduction to computer vision for a broad audience. It provides necessary theory and examples for students and practicioners who will work in fields where significant information must be extracted automatically from images. The book should be a useful resource book for professionals, a text for both undergraduate and beginning graduate courses, and a resource for enrichment of college or even high school projects. Our goals were to provide a basic set of fundamental concepts and algorithms and also discuss some of the exciting evolving application areas. This book is unique in that it contains chapters on image databases (Ch 8) and on virtual and augmented reality (Ch 15), two exciting evolving application areas. A final chapter (Ch 16) gives a complete view of real world systems that use computer vision.
Due to recent progress in the computer field, economical and flexible use of computer images is now pervasive. Computing with images is no longer just for the realm of the sciences, but also for the arts and social sciences and even for hobbyists. The book should serve an established and growing audience including those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography.
A broad purpose at first seems impossible to achieve. However, there are other kinds of texts that already do this in other areas - calculus, physics, and general computing. We hope we have made at least a good beginning - we wanted a book that would be useful in the classroom and also to the independent reader. We find the chosen topics interesting and sometimes exciting, and hope that they are accessible to a large audience. It is assumed that use of the text in a graduate, or even senior level, computer vision course would be supplemented by papers from the archival literature. Coverage is not intended to be comprehensive; only a modest set of papers are cited at the end of each chapter.
The early chapters begin at an intuitive level and progress towards mathematical models with the goal of intuitive understanding before formal characterization. Sections marked by '*' are more mathematical or more advanced and need not be covered in a less technical course. To strengthen the intuitive approach, we have stayed with the processing of iconic imagery for the first eleven chapters and have delayed 3D computer vision until the later chapters, but it should be easy for experienced instructors to resequence them to fit a particular course or teaching style. There are many viable applications that are entirely 2D, and many concepts and algorithms are more simply taught in their 2D form. We provide some basics of pattern recognition in Chapter 4, so that students can consider complete recognition systems before the full coverage of image features and matching. A reader should have a good idea of 2D image processing applications after Chapter 4; Chapters 5, 6, and 7 add in gray-tone, color, and texture features. Chapter 8 treats image databases, a popular recent topic. Although some colleagues advised us to place this material near the end of the book, our goal of positioning it early in the chapter sequence is to reinforce the concepts of the prior chapters and to provide material that can lead to an excellent half-term project. Segmentation and matching are treated in their 2D forms in Chapters 10 and 11, so that the basic concepts are presented in a simple form, without introducing the complexities of 3D transformations.