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Computer Vision : A Modern Approach

by: Forsyth, David A.; Ponce, Jean

Computer Vision : A Modern Approach

by: Forsyth, David A.; Ponce, Jean

ISBN 13:
9780130851987
ISBN 10:
0130851981
Edition: 1st
Format: Hardcover
Copyright: 01/01/2003
Publisher: Prentice Hall
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Computer Vision : A Modern Approach > ISBN13: 9780130851987

Summary

The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth.Application surveysdescribe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code.Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

I. IMAGE FORMATION.

1. Radiometry—Measuring Light.
2. Cameras.
3. Sources, Shadows, and Shading.
4. Colour.

II. IMAGE MODELS.

5. Geometric Camera Models.
6. Geometric Camera Calibration.
7. An Introduction to Probability.

III. EARLY VISION: JUST ONE IMAGE.

8. Linear Filters.
9. Edge Detection.
10. Texture.

IV. EARLY VISION: MULTIPLE IMAGES.

11. The Geometry of Multiple Views.
12. Stereopsis.
13. Affine Structure from Motion.
14. Projective Structure from Motion.

V. MID-LEVEL VISION.

15. Segmentation by Clustering.
16. Segmentation by Fitting a Model.
17. Segmentation and Fitting Using Probabilistic Methods.
18. Tracking with Linear Dynamic Models.
19. Tracking with Non-Linear Dynamic Models.

VI. HIGH-LEVEL VISION: GEOMETRIC METHODS.

20. Model-Based Vision.
21. Smooth Surfaces and Their Outlines.
22. Aspect Graphs.
23. Range Data.

VII. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS.

24. Finding Templates Using Classifiers.
25. Recognition by Relations between Templates.
26. Geometric Templates from Spatial Relations.

VIII. APPLICATIONS AND TOPICS.

27. Application: Finding in Digital Libraries.
28. Application: Image-Based Rendering.

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