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Sparse Modeling for Image and Vision Processing (Paperback)
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Sparse Modeling for Image and Vision Processing (Paperback)
Series: Foundations and Trends (R) in Computer Graphics and Vision
Expected to ship within 10 - 15 working days
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In recent years, a large amount of multi-disciplinary research has
been conducted on sparse models and their applications. In
statistics and machine learning, the sparsity principle is used to
perform model selection-that is, automatically selecting a simple
model among a large collection of them. In signal processing,
sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the
corresponding tools have been widely adopted by several scientific
communities such as neuroscience, bioinformatics, or computer
vision. Sparse Modeling for Image and Vision Processing provides
the reader with a self-contained view of sparse modeling for visual
recognition and image processing. More specifically, the work
focuses on applications where the dictionary is learned and adapted
to data, yielding a compact representation that has been successful
in various contexts. It reviews a large number of applications of
dictionary learning in image processing and computer vision and
presents basic sparse estimation tools. It starts with a historical
tour of sparse estimation in signal processing and statistics,
before moving to more recent concepts such as sparse recovery and
dictionary learning. Subsequently, it shows that dictionary
learning is related to matrix factorization techniques, and that it
is particularly effective for modeling natural image patches. As a
consequence, it has been used for tackling several image processing
problems and is a key component of many state-of-the-art methods in
visual recognition. Sparse Modeling for Image and Vision Processing
concludes with a presentation of optimization techniques that
should make dictionary learning easy to use for researchers that
are not experts in the field.
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