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Books > Computing & IT > General theory of computing
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Face Image Analysis by Unsupervised Learning (Paperback, Softcover reprint of the original 1st ed. 2001)
Loot Price: R2,420
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Face Image Analysis by Unsupervised Learning (Paperback, Softcover reprint of the original 1st ed. 2001)
Series: The Springer International Series in Engineering and Computer Science, 612
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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.
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