Computer vision : a modern approach
著者
書誌事項
Computer vision : a modern approach
（An Alan R. Apt book）
Prentice Hall, c2003
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注記
Includes bibliographical references (p. 643-671) and index
内容説明・目次
内容説明
Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.
This long anticipated book is the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
目次
I. IMAGE FORMATION AND IMAGE MODELS.
1. Cameras.
2. Geometric Camera Models.
3. Geometric Camera Calibration.
4. Radiometry - Measuring Light.
5. Sources, Shadows and Shading.
6. Color.
II. EARLY VISION: JUST ONE IMAGE.
7. Linear Filters.
8. Edge Detection.
9. Texture.
III. EARLY VISION: MULTIPLE IMAGES.
10. The Geometry of Multiple Views.
11. Stereopsis.
12. Affine Structure from Motion.
13. Projective Structure from Motion.
IV. MID-LEVEL VISION.
14. Segmentation By Clustering.
15. Segmentation By Fitting a Model.
16. Segmentation and Fitting Using Probabilistic Methods.
17. Tracking with Linear Dynamic Models.
V. HIGH-LEVEL VISION: GEOMETRIC MODELS.
18. Model-Based Vision.
19. Smooth Surfaces and Their Outlines.
20. Aspect Graphs.
21. Range Data.
VI. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS.
22. Finding Templates Using Classifiers.
23. Recognition By Relations Between Templates.
24. Geometric Templates From Spatial Relations.
VII. APPLICATIONS.
25. Application: Finding in Digital Libraries.
26. Application: Image-Based Rendering.
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