Front cover image for Machine Learning in Computer Vision

Machine Learning in Computer Vision

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models
eBook, English, 2005
Springer Netherlands, Dordrecht, 2005
1 online resource (XV, 242 pages) : online resource
9781402032752, 9781402032745, 1402032757, 1402032749
1148095115
Printed edition:
Foreword
Preface
1. Introduction
2. Theory: Probabilistic Classifiers
3. Theory: Generalization Bounds
4. Theory: Semi-Supervised Learning
5. Algorithm: Maximum Likelihood Minimum Entropy HMM
6. Algorithm: Margin Distribution Optimization
7. Algorithm: Learning The Structure Of Bayesian Network Classifiers
8. Application: Office Activity Recognition
9. Application: Multimodal Event Detection
10. Application: Facial Expression Recognition
11. Application: Bayesian Network Classifiers For Face Detection
References
Index
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