000			04164nam a22004695i 4500
999			_c387245 _d387245
001			387245
003			ES-MaUEC
005			20230218190132.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031018213
024	7		_a10.1007/978-3-031-01821-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2018 EB
100	1		_aKhan, Salman _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686982 _q(Salman Hameed)
245	1	2	_aA Guide to Convolutional Neural Networks for Computer Vision _cby Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIX, 187 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 -- Introduction -- Features and Classifiers -- Neural Networks Basics -- Convolutional Neural Network -- CNN Learning -- Examples of CNN Architectures -- Applications of CNNs in Computer Vision -- Deep Learning Tools and Libraries -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aComputer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9159793 _aVisión por ordenador _xModelos matemáticos
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9686981 _aTransformaciones (Matemáticas)
700	1		_aRahmani, Hossein _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686983
700	1		_aShah, Syed Afaq Ali _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686984
700	1		_aBennamoun, Mohammed _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031000782
776	0	8	_iPrinted edition: _z9783031006937
776	0	8	_iPrinted edition: _z9783031029493
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01821-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI