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Machine Vision

Machine Vision

Machine Vision

Wesley E. Snyder, North Carolina State University
Hairong Qi, University of Tennessee, Knoxville
November 2010
Paperback
9780521169813
$88.99
USD
Paperback
USD
eBook

    This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.

    • Describes essential theoretical background as well as cutting-edge applications
    • Includes many programming exercises that give insight into the development of image processing algorithms
    • A CD-ROM containing software and data used in the book is provided

    Product details

    November 2010
    Paperback
    9780521169813
    452 pages
    246 × 189 × 23 mm
    0.8kg
    Available

    Table of Contents

    • 1. Introduction
    • 2. Review of mathematical principles
    • 3. Writing programs to process images
    • 4. Images: description and characterization
    • 5. Linear operators and kernels
    • 6. Image relaxation: restoration and feature extraction
    • 7. Mathematical morphology
    • 8. Segmentation
    • 9. Shape
    • 10. Consistent labeling
    • 11. Parametric transform
    • 12. Graphs and graph-theoretic concepts
    • 13. Image matching
    • 14. Statistical pattern recognition
    • 15. Clustering
    • 16. Syntactic pattern recognition
    • 17. Applications
    • 18. Automatic target recognition.
    Resources for
    Type
    Supporting Materials and Errata List
    Data files for use with the book
    Size: 22.67 MB
    Type: application/zip