Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications (Advances in Computer Vision and Pattern Recognition)

Description:

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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:

Provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition
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
Researchers and graduate students interested in computer vision, pattern recognition and robotics will find this work to be an invaluable introduction to techniques of sparse representations and compressive sensing.

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eBook Details:
Category: Algorithms
Author: Hong Cheng
Language: English
ISBN10: 1447167139
ISBN13: 9781447167136
Pages: 257
PubDate: 2015-05-25 00:00:00
UploadDate: 7/24/2015 5:35:48 pm

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