Image Processing, Analysis, and Machine Vision / Milan Sonka, Vaclav Hlavac and Roger Boyle

It includes index pages and color insets etc..

Overview:

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.

Features:


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.
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.
Includes MindTap which is an interactive, customizable and complete learning solution. It includes a MindTap Reader and a library of learning apps (e.g., CNOW, Aplia, ReadSpeaker, Merriam-Webster dictionary, MyContent, RSS Feed, Kaltura, Progress app, etc.).

Table of Contents:

List of Algorithms.

Preface.

Possible Course Outlines.

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 Geometry, Correspondence, 3d from Intensities.

12. Reconstruction from 3d.

13. Mathematical Morphology.

14. Image Data Compression.

15. Texture.

16. Motion Analysis.

Index.