Visual Inference for IoT Systems: A Practical Approach

Author:   Delia Velasco-Montero ,  Jorge Fernández-Berni ,  Angel Rodríguez-Vázquez
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2022
ISBN:  

9783030909055


Pages:   159
Publication Date:   31 January 2023
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Visual Inference for IoT Systems: A Practical Approach


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Overview

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.

Full Product Details

Author:   Delia Velasco-Montero ,  Jorge Fernández-Berni ,  Angel Rodríguez-Vázquez
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2022
Weight:   0.279kg
ISBN:  

9783030909055


ISBN 10:   3030909050
Pages:   159
Publication Date:   31 January 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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