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007			cr nn 008mamaa
008			220128s2022 sz | s |||| 0|eng d
020			_a9783030909031
024	7		_a10.1007/978-3-030-90903-1 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
041			_aeng
050		4	_aTA1634
072		7	_aUT _2bicssc
072		7	_aTEC007000 _2bisacsh
072		7	_aUT _2thema
090			_aTA1634EBK
100	1		_aVelasco-Montero, Delia. _eauthor. _0(orcid)0000-0003-3487-1712 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aVisual Inference for IoT Systems: A Practical Approach _h[electronic resource] / _cby Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Embedded Vision for the Internet of the Things: State-of-the-Art -- Hardware, Software, and Network Models for Deep-Learning Vision: A Survey -- Optimal Selection of Software and Models for Visual Interference -- Relevant Hardware Metrics for Performance Evaluation -- Prediction of Visual Interference Performance -- A Case Study: Remote Animal Recognition.
520			_aThis book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements. The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed. Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
650		0	_aInternet of things.
650		0	_aComputer vision.
650		0	_aArtificial intelligence.
650	1	4	_aInternet of Things.
650	2	4	_aComputer Vision.
650	2	4	_aArtificial Intelligence.
700	1		_aFernández-Berni, Jorge. _eauthor. _0(orcid)0000-0003-0476-4676 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aRodríguez-Vázquez, Angel. _eauthor. _0(orcid)0000-0002-1006-5241 _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-90903-1 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK