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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Image Understanding using Sparse Representations (Paperback)
Loot Price: R1,079
Discovery Miles 10 790
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Image Understanding using Sparse Representations (Paperback)
Series: Synthesis Lectures on Image, Video, and Multimedia Processing
Expected to ship within 10 - 15 working days
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Total price: R1,099
Discovery Miles: 10 990
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Image understanding has been playing an increasingly crucial role
in several inverse problems and computer vision. Sparse models form
an important component in image understanding, since they emulate
the activity of neural receptors in the primary visual cortex of
the human brain. Sparse methods have been utilized in several
learning problems because of their ability to provide parsimonious,
interpretable, and efficient models. Exploiting the sparsity of
natural signals has led to advances in several application areas
including image compression, denoising, inpainting, compressed
sensing, blind source separation, super-resolution, and
classification. The primary goal of this book is to present the
theory and algorithmic considerations in using sparse models for
image understanding and computer vision applications. To this end,
algorithms for obtaining sparse representations and their
performance guarantees are discussed in the initial chapters.
Furthermore, approaches for designing overcomplete, data-adapted
dictionaries to model natural images are described. The development
of theory behind dictionary learning involves exploring its
connection to unsupervised clustering and analyzing its
generalization characteristics using principles from statistical
learning theory. An exciting application area that has benefited
extensively from the theory of sparse representations is compressed
sensing of image and video data. Theory and algorithms pertinent to
measurement design, recovery, and model-based compressed sensing
are presented. The paradigm of sparse models, when suitably
integrated with powerful machine learning frameworks, can lead to
advances in computer vision applications such as object
recognition, clustering, segmentation, and activity recognition.
Frameworks that enhance the performance of sparse models in such
applications by imposing constraints based on the prior
discriminatory information and the underlying geometrical
structure, and kernelizing the sparse coding and dictionary
learning methods are presented. In addition to presenting
theoretical fundamentals in sparse learning, this book provides a
platform for interested readers to explore the vastly growing
application domains of sparse representations.
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