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020 _a9783030909024
082 _a006.37
_bVEL
100 _aVelasco-Montero, Delia;
245 _aVisual inference for IOT systems :
_ba practical approach
_cDelia Velasco-Montero; Jorge Fernández-Berni; Angel Rodríguez-Vázquez
260 _aCham, Switzerland :
_bSpringer,
_c2022.
300 _a159 pages :
_bill.;
_c24 cm.
505 _tIntroduction --
_tEmbedded Vision for the Internet of the Things: State-of-the-Art --
_tHardware, Software, and Network Models for Deep-Learning Vision: A Survey --
_tOptimal Selection of Software and Models for Visual Interference --
_tRelevant Hardware Metrics for Performance Evaluation --
_tPrediction of Visual Interference Performance --
_tA Case Study: Remote Animal Recognition.
520 _a This 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 _aArtificial intelligence
650 _aComputer vision
650 _aInternet of things
700 _aFernández-Berni, Jorge;
700 _aRodríguez-Vázquez, Angel
942 _2ddc
_cBK
999 _c1712
_d1712