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Face Image Analysis by Unsupervised Learning (Paperback, Softcover reprint of the original 1st ed. 2001) Loot Price: R2,843
Discovery Miles 28 430
Face Image Analysis by Unsupervised Learning (Paperback, Softcover reprint of the original 1st ed. 2001): Marian Stewart...

Face Image Analysis by Unsupervised Learning (Paperback, Softcover reprint of the original 1st ed. 2001)

Marian Stewart Bartlett

Series: The Springer International Series in Engineering and Computer Science, 612

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Loot Price R2,843 Discovery Miles 28 430 | Repayment Terms: R266 pm x 12*

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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.

General

Imprint: Springer-Verlag New York
Country of origin: United States
Series: The Springer International Series in Engineering and Computer Science, 612
Release date: March 2013
First published: 2001
Authors: Marian Stewart Bartlett
Dimensions: 235 x 155 x 10mm (L x W x T)
Format: Paperback
Pages: 173
Edition: Softcover reprint of the original 1st ed. 2001
ISBN-13: 978-1-4613-5653-0
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Computing & IT > General theory of computing > General
Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction
Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
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LSN: 1-4613-5653-9
Barcode: 9781461356530

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