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001			86033
003			ES-MaUEC
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007			cr nn 008mamaa
008			160916s2016 gw | s |||| 0|eng d
020			_a9783319337623
040			_aES-MaUEC
050		4	_aTA1634 _b.K75 2016 EB
650		7	_aIngeniería civil _2embne _9138928
082	0	4	_a006.37
082	0	4	_a006.6
100	1		_aKrig, Scott _0Local _999437
245	1	0	_aComputer Vision Metrics : _bTextbook Edition _cby Scott Krig
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVIII, 637 páginas) _b331 ilustraciones, 139 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aImage Capture and Representation -- Image Re-processing -- Global and Regional Features -- Local Feature Design Concepts -- Taxonomy of Feature Description Attributes -- Interest Point Detector and Feature Descriptor Survey -- Ground Truth Data, Content, Metrics, and Analysis -- Vision Pipeline and Optimizations -- Feature Learning Architecture Taxonomy and Neuroscience Background -- Feature Learning and Deep Learning Architecture Survey. .
520			_aBased on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCVand other imaging and deep learning tools. .
710	2		_aSpringerLink (Online service) _0Local _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-33762-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319337623
907			_a.b12953118 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11614808 _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER
998			_b09/2018 _dz _ea _zSI
999			_c86033 _d86033 _x1