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008			220601s2018 sz | s |||| 0|eng d
020			_a9783031018237
024	7		_a10.1007/978-3-031-01823-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.P3 _b2018 EB
100	1		_aDana, Kristin J., _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686985 _d1968-
245	1	0	_aComputational Texture and Patterns : _bFrom Textons to Deep Learning _cby Kristin J. Dana
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIII, 99 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Acknowledgments -- Visual Patterns and Texture -- Textons in Human and Computer Vision -- Texture Recognition -- Texture Segmentation -- Texture Synthesis -- Texture Style Transfer -- Return of the Pyramids -- Open Issues in Understanding Visual Patterns -- Applications for Texture and Patterns -- Tools for Mining Patterns: Cloud Services and Software Libraries -- Bibliography -- Author's Biography.
520			_aVisual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance-to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9152614 _aReconocimiento de formas
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9159793 _aVisión por ordenador
776	0	8	_iPrinted edition: _z9783031000805
776	0	8	_iPrinted edition: _z9783031006951
776	0	8	_iPrinted edition: _z9783031029516
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01823-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI