Image Processing, Analysis, and Machine Vision 3rd Edition

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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. Salient Features Each chapter is supported by an extensive list of references and exercises. A selection of algorithms is summarized and presented formally in a manner that should aid implementation. Each chapter further includes a concise Summary section. A suggestion for partitioning the contents with possible course outlines is included in the books front matter. A full set of PowerPoint slides is available for download from this site. PowerPoints include all images and chapter summaries from the text. A stand-alone MATLAB companion book with an accompanying web page contains short answer questions and problems of varying difficulty, frequently requiring practical usage of computer tools and/or development of application programs. 25% of newly written material represented in many new sections. Chapters 2 and 3 were reorganized and enhanced to better present a broad yet not overwhelming foundation, which is used throughout the book. A brand new chapter 7 covering image segmentation methods and approaches with 3D (or higher dimension) capabilities has been added, providing 2 chapters devoted to segmentation, clearly reflecting the importance of this area. Table of Contents 1. Introduction 2. The Image, its Representations and Properties 3. The Image, its Mathematical and Physical Background 4. Data Structures for Image Analysis 5. Image Pre-Processing 6. Segmentation I 7. Segmentation II 8. Shape Representation and Description 9. Object Recognition 10. Image Understanding 11. 3D Vision, Geometry 12. Use of 3D Vision 13. Mathematical Morphology 14. Image Data Compression 15. Texture 16. Motion Analysis