Computer Vision

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

(Pearson)

Bli först med att recensera och betygsätt boken Computer Vision - du kan vinna 200 kr varje månad i tävlingen "Månadens recension".

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.

(Pearson)

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.

(Pearson)