Sparse representation for computer vision and pattern recognition
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. © 2010 IEEE.
Duke Scholars
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- 4009 Electronics, sensors and digital hardware
- 0906 Electrical and Electronic Engineering
- 0903 Biomedical Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- 4009 Electronics, sensors and digital hardware
- 0906 Electrical and Electronic Engineering
- 0903 Biomedical Engineering
- 0801 Artificial Intelligence and Image Processing