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Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
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Sparse Representation, Modeling and Learning in Visual Recognition - Theory, Algorithms and Applications (Hardcover, 2015 ed.)
Loot Price: R3,652
Discovery Miles 36 520
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Sparse Representation, Modeling and Learning in Visual Recognition - Theory, Algorithms and Applications (Hardcover, 2015 ed.)
Series: Advances in Computer Vision and Pattern Recognition
Expected to ship within 12 - 17 working days
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This unique text/reference presents a comprehensive review of the
state of the art in sparse representations, modeling and learning.
The book examines both the theoretical foundations and details of
algorithm implementation, highlighting the practical application of
compressed sensing research in visual recognition and computer
vision. Topics and features: describes sparse recovery approaches,
robust and efficient sparse representation, and large-scale visual
recognition; covers feature representation and learning, sparsity
induced similarity, and sparse representation and learning-based
classifiers; discusses low-rank matrix approximation, graphical
models in compressed sensing, collaborative representation-based
classification, and high-dimensional nonlinear learning; includes
appendices outlining additional computer programming resources, and
explaining the essential mathematics required to understand the
book.
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