- Format
- Mixed media product
- Språk
- Engelska
- Antal sidor
- 688
- Utgivningsdatum
- 2004-07-01
- Upplaga
- 1
- Förlag
- Prentice Hall
- Medarbetare
- Kang, Sing Bing
- Illustrationer
- illustrations
- Dimensioner
- 237 x 181 x 31 mm
- Vikt
- Antal komponenter
- 2
- Komponenter
- DVD-ROM (1), Paperback (1)
- ISBN
- 9780131013667
- 1230 g
Kundrecensioner
Fler böcker av Gerard Medioni
-
Tensor Voting
Philippos Mordohai, Gerard Medioni
This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount o...
Övrig information
GAeRARD MEDIONI chairs the Computer Science Department and is Professor at the Institute for Robotics and Intelligent Systems at the University of Southern California. His research interests include designing and implementing very reliable vision systems to accomplish difficult tasks and establishing bridges between computer vision and computer graphics. SING BING KANG is a member of the Interactive Visual Media Group at Microsoft Research, where he specializes in vision-based modeling. He recently co-edited Panoramic Vision: Sensors, Theory, and Applications, and has served on the technical committees of three major computer vision conferences. He holds 12 US patents.
Innehållsförteckning
Preface.
Contributors.
1. Introduction.
I. FUNDAMENTALS IN COMPUTER VISION.
2. Camera Calibration.
Zhengyou Zhang.
Introduction.
Notation and Problem Statement.
Camera Calibration with 3D Objects.
Camera Calibration with 2D Objects: Plane-Based Technique.
Solving Camera Calibration with 1D Objects.
Self-Calibration.
Conclusion.
Appendix: Estimating Homography Between Plane and Image.
Bibliography.
3. Multiple View Geometry.
Anders Heyden and Marc Pollefeys.
Introduction.
Projective Geometry.
Tensor Calculus.
Modeling Cameras.
Multiple View Geometry.
Structure and Motion I.
Structure and Motion II.
Autocalibration.
Dense Depth Estimation.
Visual Modeling.
Conclusion.
Bibliography.
4. Robust Techniques for Computer Vision.
Peter Meer.
Robustness in Visual Tasks.
Models and Estimation Problems.
Location Estimation.
Robust Regression.
Conclusion.
Bibliography.
5. The Tensor Voting Framework.
Grard Medioni and Philippos Mordohai.
Introduction.
Related Work.
Tensor Voting in 2D.
Tensor Voting in 3D.
Tensor Voting in ND.
Application to Computer Vision Problems.
Conclusion and Future Work.
Acknowledgments.
Bibliography.
II. APPLICATIONS IN COMPUTER VISION.
6. Image-Based Lighting.
Paul E. Debevec.
Basic Image-Based Lighting.
Advanced Image-Based Lighting.
Image-Based Relighting.
Conclusion.
Bibliography.
7. Computer Vision In Visual Effects.
Doug Roble.
Introduction.
Computer Vision Problems Unique to Film.
Feature Tracking.
Optical Flow.
Camera Tracking and Structure from Motion.
The Future.
Bibliography.
8. Content-Based Image Retrieval: An Overview.
Theo Gevers and Arnold W. M. Smeulders
Overview of Chapter.
Image Domains.
Image Features.
Representation and Indexing.
Similarity and Search.
Interaction and Learning.
Conclusion.
Bibliography.
9. Face Detection, Alignment, and Recognition.
Stan Z. Li and Juwei Lu.
Introduction.
Face Detection.
Face Alignment.
Face Recognition.
Bibliography.
10. Perceptual Interfaces.
Matthew Turk and Mathias Klsch
Introduction.
Perceptual Interfaces and HCI.
Multimodal Interfaces.
Vision-Based Interfaces.
Brain-Computer Interfaces.
Summary....