Emerging Topics in Computer Vision
- By Gerard Medioni, Sing Bing Kang
- Published Jul 21, 2004 by Prentice Hall. Part of the IMSC Press Multimedia Series series.
- Copyright 2005
- Dimensions: 7x9-1/4
- Pages: 688
- Edition: 1st
- ISBN-10: 0-13-101366-1
- ISBN-13: 978-0-13-101366-7
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Product Author Bios
GÉRARD 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.
The state-of-the art in computer vision: theory, applications, and programming
Whether 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-calibration
- Extracting camera motion and scene structure from image sequences
- Robust regression for model fitting using M-estimators, RANSAC, and Hough transforms
- Image-based lighting for illuminating scenes and objects with real-world light images
- Content-based image retrieval, covering queries, representation, indexing, search, learning, and more
- Face detection, alignment, and recognition--with new solutions for key challenges
- Perceptual interfaces for integrating vision, speech, and haptic modalities
- Development with the Open Source Computer Vision Library (OpenCV)
- The new SAI framework and patterns for architecting computer vision applications
3 of 3 people found the following review helpful
Good supplemental text on computer vision,
This review is from: Emerging Topics in Computer Vision (Paperback)This is a good book as long as you realize it is intended to be a supplement to a good basic text on the topic of computer vision and not a textbook itself. As such, it is not a good source of algorithms. Instead, it takes a high level approach and discusses topics that computer vision textbooks don't have room to include. This is not to say that the book is page after page of narrative with no instruction on specific steps whatsoever. It is just not full of the matrices, transforms, and algorithms you would expect in a textbook on the subject. For example, when this book discusses using a neural network for some computer vision task, it is assumed you already know how to set up a neural network to solve a problem via MATLAB or some alternate method and that you know what kind of problems neural networks can aid in solving, where a computer vision textbook would likely go over the subject and science of neural networks separate from the specific concern of computer vision. The book is... Read more
1 of 1 people found the following review helpful
CD not included,
Amazon Verified Purchase(What's this?)
This review is from: Emerging Topics in Computer Vision (Paperback)While the other reviews do justice to the content of the book, they do not mention one possibly important point. Though this book was supposed to come with a CD, it does not. Inside the book, the publisher states that the CD contents have been moved online, but the content is not actually available on the publisher's site.
After communicating with the publisher about this issue, I have learned that the CD content has been lost. Do not count on being able to access the supplement to the text (program code examples, etc.).
some topics are mature; others might be obsolete,
This review is from: Emerging Topics in Computer Vision (Paperback)For machine vision researchers, the editors of the book compiled a good survey of the field in 2004. The book does not start from scratch, unlike Machine Vision by Davies. Instead, it dives straight into numerous topics, by assuming you are already versed in the basics.
The text has a combination of descriptions of the maths underlining the methods, and the showing of the results from applying the methods.
Some topics are by now fairly mature. Take image based lighting, where scenes are illuminated by one or more light sources. For realistic renderings, the methods described should give very good results.
Face detection, on the other hand, still has ways to go. The chapter on it talks about using Haar feature sets and other ideas. But the chapter may have been somewhat obsoleted by recent  work that used another method that is orders of magnitude faster, though with roughly the same accuracy.
› See all 3 customer reviews...
Table of Contents
I. FUNDAMENTALS IN COMPUTER VISION.
2. Camera Calibration.
Notation and Problem Statement.
Camera Calibration with 3D Objects.
Camera Calibration with 2D Objects: Plane-Based Technique.
Solving Camera Calibration with 1D Objects.
Appendix: Estimating Homography Between Plane and Image.
3. Multiple View Geometry.
Anders Heyden and Marc Pollefeys.
Multiple View Geometry.
Structure and Motion I.
Structure and Motion II.
Dense Depth Estimation.
4. Robust Techniques for Computer Vision.
Robustness in Visual Tasks.
Models and Estimation Problems.
5. The Tensor Voting Framework.
Gérard Medioni and Philippos Mordohai.
Tensor Voting in 2D.
Tensor Voting in 3D.
Tensor Voting in ND.
Application to Computer Vision Problems.
Conclusion and Future Work.
II. APPLICATIONS IN COMPUTER VISION.
6. Image-Based Lighting.
Paul E. Debevec.
Basic Image-Based Lighting.
Advanced Image-Based Lighting.
7. Computer Vision In Visual Effects.
Computer Vision Problems Unique to Film.
Camera Tracking and Structure from Motion.
8. Content-Based Image Retrieval: An Overview.
Theo Gevers and Arnold W. M. Smeulders
Overview of Chapter.
Representation and Indexing.
Similarity and Search.
Interaction and Learning.
9. Face Detection, Alignment, and Recognition.
Stan Z. Li and Juwei Lu.
10. Perceptual Interfaces.
Matthew Turk and Mathias Kölsch
Perceptual Interfaces and HCI.
III. PROGRAMMING FOR COMPUTER VISION.
11. Open Source Computer Vision Library.
Functional Groups: What's Good for What.
Programming Examples Using C/C++.
12. Software Architecture For Computer Vision.
Alexandre R. J. François.
SAI: A Software Architecture Model.
MFSM: An Architectural Middleware.
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