000 | 04690nam a22005415i 4500 | ||
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001 | 978-3-031-02250-0 | ||
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007 | cr nn 008mamaa | ||
008 | 220601s2014 sz | s |||| 0|eng d | ||
020 |
_a9783031022500 _9978-3-031-02250-0 |
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024 | 7 |
_a10.1007/978-3-031-02250-0 _2doi |
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050 | 4 | _aT1-995 | |
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_a620 _223 |
100 | 1 |
_aThiagarajan, Jayaraman J. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980861 |
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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. |
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300 |
_aXI, 106 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
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650 | 0 |
_aElectrical engineering. _980862 |
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650 | 0 |
_aSignal processing. _94052 |
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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 |
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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 |
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