Jorge Fernández-Berni – författare

This book presents a comprehensive, systematic approach to the development of vision system architectures that employ sensory-processing concurrency and parallel processing to meet the autonomy challenges posed by a variety of safety and surveillance applications.  Coverage includes a thorough analysis of resistive diffusion networks embedded within an image sensor array. This analysis supports a systematic approach to the design of spatial image filters and their implementation as vision chips in CMOS technology. The book also addresses system-level considerations pertaining to the embedding of these vision chips into vision-enabled wireless sensor networks.

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.