Machine learning in computer vision [electronic resource] / by N. Sebe [and others].
- Dordrecht : Springer, 2005.
- Physical Description:
- 1 online resource (xv, 240 pages) : illustrations
- Additional Creators:
- Sebe, Nicu
- Computational imaging and vision ; v. 29
- Annotation "This book comes right on time ... It is amazing so early in a new field that a book appears which connects theory to algorithms and through them to convincing applications ... This book will surely be with us for quite some time to come."€ € € € € € € € € € From the foreword by Arnold Smeulders 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. This book is intended for computer vision, machine learning, and pattern recognition researchers as well as for graduate students in computer science and electrical engineering. € €
- 9781402032752, 1402032757, 9786610283590, 6610283591, 1402032749 (Cloth), and 9781402032745 (Cloth)
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
- Bibliography Note:
- Includes bibliographical references (pages -236) and index.
- Reproduction Note:
- Electronic reproduction. Berlin : Springer, 2005. Mode of access: World Wide Web. Available via SpringerLink.
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