TY - JOUR
T1 - Applications of sparse representation and compressive sensing
AU - Baraniuk, Richard G.
AU - Candes, Emmanuel
AU - Elad, Michael
AU - Ma, Yi
N1 - Funding Information:
Dr. Baraniuk received a NATO postdoctoral fellowship from NSERC in 1992, the National Young Investigator award from the National Science Foundation in 1994, a Young Investigator Award from the Office of Naval Research in 1995, the Rosenbaum Fellowship from the Isaac Newton Institute of Cambridge University in 1998, the C. Holmes MacDonald National Outstanding Teaching Award from Eta Kappa Nu in 1999, the University of Illinois ECE Young Alumni Achievement Award in 2000, the Tech Museum Laureate Award from the Tech Museum of Innovation in 2006, the Wavelet Pioneer Award from SPIE in 2008, the Internet Pioneer Award from the Berkman Center for Internet and Society at Harvard Law School in 2008, and the World Technology Network Education Award in 2009. In 2007, he was selected as one of Edutopia Magazine’s Daring Dozen educators, and the Rice single-pixel compressive camera was selected by MIT Technology Review Magazine as a TR10 Top 10 Emerging Technology. He was elected an IEEE Fellow in 2001 and an American Association for the Advancement of Science (AAAS) Fellow in 2009.
Funding Information:
Dr. Ma was the recipient of the David Marr Best Paper Prize at the International Conference on Computer Vision in 1999, the Longuet-Higgins Best Paper Award at the European Conference on Computer Vision in 2004, and the Sang Uk Lee Best Student Paper Award at the Asian Conference on Computer Vision in 2009. He received the CAREER Award from the National Science Foundation in 2004 and the Young Investigator Program Award from the Office of Naval Research in 2005. He has given several plenary lectures at international conferences. He currently serves as an Associate Editor for the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. He is a member of the Association for Computing Machinery (ACM) and the Society of Industrial and Applied Mathematics (SIAM).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2010/6
Y1 - 2010/6
N2 - Applications of sparse representation and compressive sensing are discussed. A sparse signal is a signal that can be represented as a linear combination of relatively few base elements in a basis or an over complete dictionary. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms. The papers aim to provide good survey or review of past achievements in the field, or feature some new exciting developments by the authors, or discuss promising new directions and extensions. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms.
AB - Applications of sparse representation and compressive sensing are discussed. A sparse signal is a signal that can be represented as a linear combination of relatively few base elements in a basis or an over complete dictionary. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms. The papers aim to provide good survey or review of past achievements in the field, or feature some new exciting developments by the authors, or discuss promising new directions and extensions. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms.
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U2 - 10.1109/JPROC.2010.2047424
DO - 10.1109/JPROC.2010.2047424
M3 - Article
AN - SCOPUS:77952678579
VL - 98
SP - 906
EP - 909
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
SN - 0018-9219
IS - 6
M1 - 5466604
ER -