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Computer Vision : A Modern Approach
ISBN: 9780130851987 | 0130851981
Edition: 1stFormat: Hardcover
Publisher: Prentice Hall
Pub. Date: 1/1/2003
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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 pr... MORE
I. IMAGE FORMATION.
1. Radiometry—Measuring Light.
2. Cameras.
3. Sources, Shadows, and Shading.
4. Colour.
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.
6. Geometric Camera Calibration.
7. An Introduction to Probability.
III. EARLY VISION: JUST ONE IMAGE.
IV. EARLY VISION: MULTIPLE IMAGES.
11. The Geometry of Multiple Views.
12. Stereopsis.
13. Affine Structure from Motion.
14. Projective Structure from Motion.
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.
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.
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.
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.
28. Application: Image-Based Rendering.
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