Velasco-Montero, Delia;  <br/><br/>
Visual inference for IOT systems :   a practical approach 
Delia Velasco-Montero; Jorge Fernández-Berni; Angel Rodríguez-Vázquez 
 - Cham, Switzerland :   Springer,   2022. 
 - 159 pages :  ill.;  24 cm. 
<br/><br/> Introduction --<br/>  Embedded Vision for the Internet of the Things: State-of-the-Art --<br/>  Hardware, Software, and Network Models for Deep-Learning Vision: A Survey --<br/>  Optimal Selection of Software and Models for Visual Interference --<br/>  Relevant Hardware Metrics for Performance Evaluation --<br/>  Prediction of Visual Interference Performance --<br/>  A Case Study: Remote Animal Recognition. 
<br/><br/> 	<br/>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.  
<br/><br/><label>ISBN: </label>9783030909024 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence<br/>Computer vision<br/>Internet of things
<br/><br/><label>Dewey Class. No.: </label>006.37  / VEL