Face Image Analysis by Unsupervised Learning
Language: English
Published by Springer Nature B.V. Jun 2001, 2001
Series: Book 169 of 260 - The Springer International Series in Engineering and Computer Science
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 199.18
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Neuware - Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.…
Seller Inventory # 9780792373483
- Title
- Face Image Analysis by Unsupervised Learning
- Author
- Marian Stewart Bartlett
- Publisher
- Springer Nature B.V. Jun 2001
- Publication year
- 2001
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0792373480
- ISBN 13
- 9780792373483
- Item weight
- 465 grams
- Dimensions
- 241x160x16 mm
- Series
- Book 169 of 260: The Springer International Series in Engineering and Computer Science
The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition.
Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
"Synopsis" may belong to another edition of this title.
Review
T.J. Sejnowski, Salk Institute
"About the title" may belong to another edition of this title.
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