Simon J. D. Prince

Computer Vision (eBook, ePUB)

Models, Learning, and Inference

• Format: ePub

Produktbeschreibung

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. * Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry * A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking * More than 70 algorithms are described in sufficient detail to implement * More than 350 full-color illustrations amplify the text * The treatment is self-contained, including all of the background mathematics * Additional resources at www.computervisionmodels.com

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 18. Juni 2012
• Englisch
• ISBN-13: 9781139506304
• Artikelnr.: 52928799

Autorenporträt

Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and HCI.

Inhaltsangabe

Part I. Probability: 1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision: 5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models: 9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing: 12. Image preprocessing and feature extraction; Part V. Models for Geometry: 13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision: 16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices: A. Optimization; B. Linear algebra; C. Algorithms.

Rezensionen

'Computer vision and machine learning have married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of 'aha!' moments as I read through the book. This is an important book for computer vision researchers and students, and I look forward to teaching from it.' William T. Freeman, Massachusetts Institute of Technology