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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.
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John Wright
Yi Ma
Julien Mairal
Proceedings of the IEEE
Centre National de la Recherche Scientifique
University of Minnesota
University of Illinois Urbana-Champaign
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Wright et al. (Mon,) studied this question.
www.synapsesocial.com/papers/6a12cdc9257f24f1de9e5122 — DOI: https://doi.org/10.1109/jproc.2010.2044470
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