Sparse Representations and Compressive Sensing for Imaging and Vision
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Sparse Representations and Compressive Sensing for Imaging and Vision

 eBook
Sofort lieferbar | Lieferzeit: Sofort lieferbar I
ISBN-13:
9781461463818
Veröffentl:
2013
Einband:
eBook
Seiten:
102
Autor:
Vishal M. Patel
Serie:
SpringerBriefs in Electrical and Computer Engineering
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

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.
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.
Introduction.- Compressive Sensing.- Compressive Acquisition.- Compressive Sensing for Vision.- Sparse Representation-based Object Recognition.- Dictionary Learning.- Concluding Remarks.

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