Buch, Englisch, 159 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g
ISBN: 978-3-030-90905-5
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
Weitere Infos & Material
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.
