Visual Inference for IoT Systems: A Practical Approach 1st ed. 2022 Edition

Visual Inference for IoT Systems: A Practical Approach 1st ed. 2022 Edition book cover

Visual Inference for IoT Systems: A Practical Approach 1st ed. 2022 Edition

Author(s): Delia Velasco-Montero (Author), Jorge Fernández-Berni (Author), Angel Rodríguez-Vázquez (Author)

  • Publisher: Springer
  • Publication Date: 31 Jan. 2023
  • Edition: 1st ed. 2022
  • Language: English
  • Print length: 172 pages
  • ISBN-10: 3030909050
  • ISBN-13: 9783030909055

Book Description

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.

Editorial Reviews

From the Back Cover

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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