Sparse Representations and Compressive Sensing for Imaging and Vision

• Main Authors: Patel, Vishal M. (Author), Chellappa, Rama. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2013.
• Series: SpringerBriefs in Electrical and Computer Engineering,
• Subjects: Engineering.; Computer vision.; Signal, Image and Speech Processing.; Image Processing and Computer Vision.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6381-8
• Physical Description: X, 102 p. 41 illus. online resource.
• ISBN: 9781461463818
• ISSN: 2191-8112

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.