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008			221119s2022 sz | s |||| 0|eng d
020			_a9783030909871
024	7		_a10.1007/978-3-030-90987-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2022 EB
100	1		_aBetti, Alessandro, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685372 _d1977-
245	1	0	_aDeep Learning to See : _bTowards New Foundations of Computer Vision _cby Alessandro Betti, Marco Gori, Stefano Melacci
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIV, 105 páginas) _b13 ilustraciones, 3 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_a1. Introduction -- 2. Cutting the Umbilical Cord with Pattern Recognition -- 3. Spatiotemporal Visual Environments -- 4. Hierarchical Description of Visual Tasks -- 5. Benchmarks and the "En Plein Air" Challenge.
520			_aThe remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this book criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. Specifically, the present work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. This unique volume will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.
988			_aSpringer_Computer_2022
650		7	_2embne _9159793 _aVisión por ordenador _vCongresos y asambleas
650		7	_2embne _9166090 _aAprendizaje automático _vCongresos y asambleas
700	1		_aGori, Marco _eautor _0(orcid)0000-0001-6337-5430 _1https://orcid.org/0000-0001-6337-5430 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685373
700	1		_aMelacci, Stefano _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685374
776	0	8	_iPrinted edition: _z9783030909864
776	0	8	_iPrinted edition: _z9783030909888
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-90987-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI