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Sparse Representations and Compressive Sensing for Imaging and Vision [electronic resource] / by Vishal M. Patel, Rama Chellappa.

By: Patel, Vishal M [author.].
Contributor(s): Chellappa, Rama [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Electrical and Computer Engineering: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: X, 102 p. 41 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461463818.Subject(s): Engineering | Computer vision | Engineering | Signal, Image and Speech Processing | Image Processing and Computer VisionDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Compressive Sensing -- Compressive Acquisition -- Compressive Sensing for Vision -- Sparse Representation-based Object Recognition -- Dictionary Learning -- Concluding Remarks.
In: Springer eBooksSummary: Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.
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Introduction -- Compressive Sensing -- Compressive Acquisition -- Compressive Sensing for Vision -- Sparse Representation-based Object Recognition -- Dictionary Learning -- Concluding Remarks.

Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

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