ISBN-10:
1133593607
ISBN-13:
9781133593607
Pub. Date:
01/01/2014
Publisher:
CL Engineering
Image Processing, Analysis, and Machine Vision / Edition 4

Image Processing, Analysis, and Machine Vision / Edition 4

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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.

Product Details

ISBN-13: 9781133593607
Publisher: CL Engineering
Publication date: 01/01/2014
Series: MindTap Course List Series
Edition description: New Edition
Pages: 912
Sales rank: 989,636
Product dimensions: 8.20(w) x 9.40(h) x 1.30(d)

About the Author

Milan Sonka is Professor of Electrical and Computer Engineering at the University of Iowa. His research interests include medical image analysis, computer-aided diagnosis, and machine vision.


Vaclav Hlavac is Professor of Cybernetics at the Czech Technical University, Prague. his research interests are knowledge based image analysis, 3D model-based vision and relations between statistical and structural pattern recognition.


Roger Boyle is Professor Emeritus of Computing and was Head of the School of Computing at the University of Leeds, England where his research interests are low-level vision and pattern recognition.

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.

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