| Description |
xxxiii, 934 pages : illustrations ; 24 cm |
| Contents |
Machine derived contents note: 1. Vision, the Challenge -- Part 1 Low-Level Vision -- 2. Images and Imaging Operations -- 3. Basic Image Filtering Operations -- 4. Thresholding Techniques -- 5. Edge Detection -- 6. Binary Shape Analysis -- 7. Boundary Pattern Analysis -- 8. Mathematical Morphology -- Part 2 Intermediate-Level Vision -- 9. Line Detection -- 10. Circle Detection -- 11. The Hough Transform and Its Nature -- 12. Ellipse Detection -- 13. Hole Detection -- 14. Polygon and Corner Detection -- 15. Abstract Pattern Matching Techniques -- Part 3 3?D Vision and Motion -- 16. The Three-Dimensional World -- 17. Tackling the Perspective n-Point Problem -- 18. Motion -- 19. Invariants and their Applications -- 20. Egomotion and Related Tasks -- 21. Image Transformations and Camera Calibration -- Part 4 Towards Real-Time Pattern Recognition Systems -- 22. Automated Visual Inspection -- 23. Inspection of Cereal Grains -- 24. Statistical Pattern Recognition -- 25. Biologically Inspired Recognition Schemes -- 26. Texture -- 27. Image Acquisition -- 28. Real-Time Hardware and Systems Design Considerations -- Part 5 Perspectives on Vision -- 29. Machine Vision, Art or Science? -- Appendix A Robust Statistics |
| Summary |
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need. As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems |
| Notes |
Formerly CIP. Uk |
|
Previous ed.: 1996 |
| Bibliography |
Includes bibliographical references (pages 869-915) and index |
| Subject |
Computer algorithms.
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Computer vision.
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Image processing.
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Pattern recognition schemes
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| LC no. |
2005270146 |
| ISBN |
0122060938 |
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