Synopses & Reviews
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 theoreticallythe 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 SurveysNumerous examples, including Image Based Rendering and Digital Libraries
- Boxed AlgorithmsKey algorithms broken out and illustrated in pseudo code
- Self-ContainedNo need for other references
- Extensive, Detailed IllustrationsExamples of inputs and outputs for current methods
- Programming Assignments50 programming assignments and 150 exercises
Synopsis
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 theoreticallythe 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 SurveysNumerous examples, including Image Based Rendering and Digital Libraries
- Boxed AlgorithmsKey algorithms broken out and illustrated in pseudo code
- Self-ContainedNo need for other references
- Extensive, Detailed IllustrationsExamples of inputs and outputs for current methods
- Programming Assignments50 programming assignments and 150 exercises
Synopsis
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 surveys describe 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.
About the Author
David A. Forsyth received the D.Phil. degree in computer science from Oxford University. He is currently a Professor in the Computer Science Division at the University of California at Berkeley. He has co-authored over eighty technical papers on computer vision, computer graphics and machine learning and has co-edited two books.
Jean Ponce received the Ph.D. degree in Computer Science from the University of Paris Orsay. He is currently a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana Champaign. Professor Ponce has written over a hundred conference and journal papers and co-edited two books on a range of subjects including computer vision and robotics.
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.