Sparse representations and compressive sensing for imaging and vision /

Sparse representations and compressive sensing for imaging and vision /

Saved in:

Bibliographic Details
Author / Creator:	Patel, Vishal M.
Imprint:	Dordrecht : Springer, 2013.
Description:	1 online resource (110 p.)
Language:	English
Series:	SpringerBriefs in Electrical and Computer Engineering SpringerBriefs in Electrical and Computer Engineering.
Subject:	Signal processing -- Digital techniques -- Mathematics. Computer vision -- Mathematics. Computer vision. Engineering.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/9849851

Hidden Bibliographic Details
Other authors / contributors:	Chellappa, Rama.
ISBN:	9781461463818 (electronic bk.) 1461463815 (electronic bk.) 9781461463801
Notes:	Includes bibliographical references. Description based on print version record.
Summary:	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.
Other form:	Print version: Patel, Vishal M. Sparse Representations and Compressive Sensing for Imaging and Vision Dordrecht : Springer, c2013 9781461463801

Description
Summary:	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.
Physical Description:	1 online resource (110 p.)
Bibliography:	Includes bibliographical references.
ISBN:	9781461463818 (electronic bk.) 1461463815 (electronic bk.) 9781461463801