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The state-of-the art in computer visionprogrammingWhether you're a working engineer, developer, researcher, or student, this is your single authoritative source for today's key computer vision innovations. Gerard Medioni and Sing Bing Kang present advances in computer vision such as camera calibration, multi-view geometry, and face detection, and introduce important new topics such as vision for special effects and the tensor voting framework. They begin with the fundamentals, cover select applications in detail, and introduce two popular approaches to computer vision programming.Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibrationExtracting camera motion and scene structure from image sequencesRobust regression for model fitting using M-estimators, RANSAC, and Hough transformsImage-based lighting for illuminating scenes and objects with real-world light imagesContent-based image retrieval, covering queries, representation, indexing, search, learning, and moreFace detection, alignment, and recognition--with new solutions for key challengesPerceptual interfaces for integrating vision, speech, and haptic modalitiesDevelopment with the Open Source Computer Vision Library (OpenCV)The new SAI framework and patterns for architecting computer vision applications
Contents
Preface.Contributors.1. Introduction. 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.Gerard 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. 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. SmeuldersOverview 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 KoelschIntroduction.Perceptual Interfaces and HCI.Multimodal Interfaces.Vision-Based Interfaces.Brain-Computer Interfaces.Summary.Bibliography. III. PROGRAMMING FOR COMPUTER VISION. 11. Open Source Computer Vision Library.Gary Bradski.Overview.Functional Groups: What's Good for What.Pictorial Tour.Programming Examples Using C/C++.Other Interfaces.Appendix A.Appendix B.Bibliography.12. Software Architecture For Computer Vision. Alexandre R. J. Francois. Introduction.SAI: A Software Architecture Model.MFSM: An Architectural Middleware.Conclusion.Acknowledgments.Bibliography.Index.
