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The brand new edition of IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION is a robust text providing 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.
- The "Problems and Exercises" part of each chapter has been updated and moved back to the book, rather than being kept in the MATLAB Companion.
- The new edition retains the same Chapter structure, but many sections have been rewritten or introduced as new -- 15% of this new edition consists of newly written material presenting state-of-the-art methods and techniques that have already proven their importance in the field.
- Among the new topics are Radon transform, unified approach to image/template matching, efficient object skeletonization (MB and MB2 algorithms), nearest neighbor classification including BBF/FLANN, random forests, Markov random fields, Gaussian mixture models–expectation maximization, scale invariant feature transform (SIFT), recent 3D image analysis/vision development, texture description using local binary patterns, and several point tracking approaches for motion analysis.
- Chapter 12 has been entirely rewritten.
- Approaches to 3D vision has been heavily revised.
- 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.
- 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.
- Reflects the authors' experience in teaching one and two semester undergraduate courses in Digital Image Processing, Digital Image Analysis, Image Understanding, Medical Imaging, Machine Vision, Pattern Recognition, and Intelligent Robotics at their respective institutions.
- Each chapter further includes a concise Summary section.
Possible Course Outlines.
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 Geometry, Correspondence, 3d from Intensities.
12. Reconstruction from 3d.
13. Mathematical Morphology.
14. Image Data Compression.
16. Motion Analysis.
"...intuitive and easy to understand, and is well structured. Concepts are clearly explained and illustrated with images and diagrams."
"Many of these problems refer to real-world applications, which makes them particularly interesting to the readers."
Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.
Find everything you need for your course all in one place. All instructor resources for this book are available instantly through the password-protected Instructor portion of the Companion Website, the Instructor’s Solutions Manual, Lecture Note PowerPoint® slides, algorithmic pseudocode and processed images in PowerPoint slides.
Instructor's Solution Manual
This carefully reviewed and accurate Solution Manual saves you time as it provides complete solutions to all problems in the book.