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020 _a9783031022500
_9978-3-031-02250-0
024 7 _a10.1007/978-3-031-02250-0
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
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082 0 4 _a620
_223
100 1 _aThiagarajan, Jayaraman J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980861
245 1 0 _aImage Understanding using Sparse Representations
_h[electronic resource] /
_cby Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXI, 106 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
505 0 _aIntroduction -- Sparse Representations -- Dictionary Learning: Theory and Algorithms -- Compressed Sensing -- Sparse Models in Recognition -- Bibliography -- Authors' Biographies .
520 _aImage 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.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_980862
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_980863
650 2 4 _aElectrical and Electronic Engineering.
_980864
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aRamamurthy, Karthikeyan Natesan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980865
700 1 _aTuraga, Pavan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980866
700 1 _aSpanias, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980867
710 2 _aSpringerLink (Online service)
_980868
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011221
776 0 8 _iPrinted edition:
_z9783031033780
830 0 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
_980869
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02250-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c85056
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