Computer Vision: Models, Learning, and Inference

Full PDF book of “Computer Vision: Models, Learning, and Inference” by Simon J.D. Prince is available for free. It is primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision.

Book Description

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.

Table of Contents

• Introduction
• Introduction to probability
• Fitting probability models
• The normal distribution
• Learning and inference in vision
• Modeling complex data densities
• Regression models
• Classification models
• Graphical models
• Models for chains and trees
• Models for grids
• Image preprocessing and feature extraction
• The pinhole camera
• Models for transformations
• Multiple cameras
• Models for shape
• Models for style and identity
• Temporal models
• Models for visual words

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