TY - JOUR
T1 - Sparse representation for computer vision and pattern recognition
AU - Wright, John
AU - Ma, Yi
AU - Mairal, Julien
AU - Sapiro, Guillermo
AU - Huang, Thomas S.
AU - Yan, Shuicheng
N1 - Funding Information:
Manuscript received March 25, 2009; revised December 29, 2009; accepted February 3, 2010. Date of publication April 29, 2010; date of current version May 19, 2010. The work of J. Wright and Y. Ma was supported in part by NSF, ONR, and a Microsoft Fellowship. The work of G. Sapiro was supported in part by ONR, NGA, NSF, DARPA, and ARO. The work of T. S. Huang was supported in part by IARPA VACE Program. The work of S. Yan was supported in part by the NRF/IDM under Grant NRF2008IDM-IDM004-029. J. Wright was with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA. He is now with Visual Computing Group, Microsoft Research Asia, Beijing 100190, China (e-mail: [email protected]). Y. Ma is with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA, and also with the Visual Computing Group, Microsoft Research Asia, Beijing 100190, China (e-mail: [email protected]). J. Mairal is with the INRIA-Willow project, Ecole Normale Supérieure, Laboratoire d’Informatique de l’Ecole Normale Supérieure (INRIA/ENS/CNRS UMR 8548), 75005 Paris, France (e-mail: [email protected]). G. Sapiro is with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: [email protected]). T. S. Huang is with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA (e-mail: [email protected]). S. Yan is with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (e-mail: [email protected]).
Funding Information:
Dr. Ma received the David Marr Best Paper Prize at the International Conference on Computer Vision 1999, the Longuet–Higgins Best Paper Prize at the European Conference on Computer Vision 2004, and the Sang Uk Lee Best Student Paper Award with his students at the Asian Conference on Computer Vision in 2009. He also received the CAREER Award from the National Science Foundation in 2004 and the Young Investigator Award from the Office of Naval Research in 2005. He is an associate editor of the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE and has served as the chief guest editor for special issues for the PROCEEDINGS OF IEEE and the IEEE SIGNAL PROCESSING MAGAZINE. He will also serve as Program Chair for the 2013 International Conference on Computer Vision, Sydney, Australia. He is a member of the Association for Computing Machinery (ACM), the Society for Industrial and Applied Mathematics (SIAM), and the American Society for Engineering Education (ASEE).
Funding Information:
Dr. Sapiro recently coedited a special issue of the IEEE TRANSACTIONS ON IMAGE PROCESSING and a second one in the Journal of Visual Communication and Image Representation. He was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991, the Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992, the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, and the National Science Foundation Career Award in 1999. He is a member of the Society for Industrial and Applied Mathematics (SIAM). He is the funding Editor-in-Chief of the SIAM Journal on Imaging Sciences.
PY - 2010/6
Y1 - 2010/6
N2 - Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
AB - Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
KW - Compressed sensing
KW - Computer vision
KW - Pattern recognition
KW - Signal representations
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U2 - 10.1109/JPROC.2010.2044470
DO - 10.1109/JPROC.2010.2044470
M3 - Article
AN - SCOPUS:77952717202
SN - 0018-9219
VL - 98
SP - 1031
EP - 1044
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 6
M1 - 5456194
ER -