Computer Vision

by Prince, Simon J. D.

• ISBN13: 9781107011793
• ISBN10: 1107011795
• Format: Hardcover
• Copyright: 2012-06-18
• Publisher: Cambridge Univ Pr

Summary

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.

Table of Contents

Probability

Introduction to probability

Common probability distributions

Fitting probability models

The normal distribution

Machine Learning for Machine Vision

Learning and inference in vision

Modeling complex data densities

Regression models

Classification models

Connecting Local Models

Graphical models

Models for chains and trees

Models for grids

Preprocessing

Image preprocessing and feature extraction

Models for Geometry

The pinhole camera

Models for transformations

Multiple cameras

Models for Vision

Models for style and identity

Temporal models

Models for visual words

Appendices

Optimization

Linear algebra

Algorithms

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