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Visual inference for IoT systems = a...

Velasco-Montero, Delia.

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Visual inference for IoT systems = a practical approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Visual inference for IoT systems/ by Delia Velasco-Montero, Jorge Fernandez-Berni, Angel Rodriguez-Vazquez.
其他題名:	a practical approach /
作者:	Velasco-Montero, Delia.
其他作者:	Fernandez-Berni, Jorge.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xiii, 159 p. :ill., digital ;24 cm.
內容註:	Introduction -- 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.
Contained By:	Springer Nature eBook
標題:	Computer vision. -
電子資源:	https://doi.org/10.1007/978-3-030-90903-1
ISBN:	9783030909031

Visual inference for IoT systems = a practical approach /
Velasco-Montero, Delia.

Visual inference for IoT systemsa practical approach /[electronic resource] :by Delia Velasco-Montero, Jorge Fernandez-Berni, Angel Rodriguez-Vazquez. - Cham :Springer International Publishing :2022. - xiii, 159 p. :ill., digital ;24 cm.

Introduction -- 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.

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.

ISBN: 9783030909031

Standard No.: 10.1007/978-3-030-90903-1doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634 / .V45 2022

Dewey Class. No.: 006.37

Visual inference for IoT systems = a practical approach /
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505 0 $a Introduction -- 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 $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.
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