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Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications

Cheng, Hong

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ISBN 10: 1447167139 / ISBN 13: 9781447167136
Editorial: Springer-Verlag New York Inc, 2015
Nuevos Condición: Brand New Encuadernación de tapa dura
Librería: Revaluation Books (Exeter, Reino Unido)

Librería en AbeBooks desde: 6 de enero de 2003

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9.25x6.25x0.75 inches. In Stock. N° de ref. de la librería __1447167139

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Detalles bibliográficos

Título: Sparse Representation, Modeling and Learning...

Editorial: Springer-Verlag New York Inc

Año de publicación: 2015

Encuadernación: Hardcover

Condición del libro:Brand New

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Sinopsis:

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.

From the Back Cover:

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.

Dr. Hong Cheng is Professor in the School of Automation Engineering, and Deputy Executive  Director of the Center for Robotics at the University of Electronic Science and Technology of China. His other publications include the Springer book Autonomous Intelligent Vehicles.

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Business correspondence address: Exstowe, Exton, Exeter, EX3 0PP
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