Title: Learning sparse representations to restore, classify, and sense images and videos
Speaker: Guillermo Sapiro, University of Minnesota
Date: Thursday, May 22 2008
Time: 4:00PM to 5:30PM
Location: 32-G449(Patil)
Sparse representations have recently drawn much attention from the signal processing and learning communities. The basic underlying model consist of considering that natural images, or signals in general, admit a sparse decomposition in some redundant dictionary. This means that we can find a linear combination of a few atoms from the dictionary that lead to an efficient representation of the original signal. Recent results have shown that learning overcomplete non-parametric dictionaries for image representation, instead of using off-the-shelf ones, significantly improves numerous image and video processing tasks.
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