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Computer Vision: A Modern Approach
SubjectComputer Vision
ISBN/SKU0130851981
AuthorDavid A. Forsyth, Jean Ponce
PublisherPrentice Hall PTR
Publish DateAugust 2002
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Summary

Computer Vision:
A Modern Approach

This extraordinary book gives a uniquely modern view of computer vision. Offering a general survey of the whole computer vision enterprise along with sufficient detail for readers to be able to build useful applications, this book is invaluable in providing a strategic overview of computer vision. With extensive use of probabalistic methods— topics have been selected for their importance, both practically and theoretically—the book gives the most coherent possible synthesis of current views, emphasizing techniques that have been successful in building applications. Readers engaged in computer graphics, robotics, image processing, and imaging in general will find this text an informative reference.

KEY FEATURES

  • Application Surveys—Numerous examples, including Image Based Rendering and Digital Libraries
  • Boxed Algorithms—Key algorithms broken out and illustrated in pseudo code
  • Self-Contained—No need for other references
  • Extensive, Detailed Illustrations—Examples of inputs and outputs for current methods
  • Programming Assignments—50 programming assignments and 150 exercises
Table of Contents

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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