Low-Power Smart Imagers for Vision-Enabled Sensor Networks

2012
ISBN: 978-1-4614-2391-1
Verlag: Springer

Buch, Englisch, 156 Seiten, HC gerader Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 442 g

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.
 Describes a system-level approach for designing of vision devices and  embedding them into vision-enabled, wireless sensor networks; Surveys state-of-the-art, vision-enabled WSN nodes; Includes details of specifications and challenges of vision-enabled WSNs; Explains architectures for low-energy CMOS vision chips with embedded, programmable spatial filtering capabilities; Includes considerations pertaining to the integration of vision chips into off-the-shelf WSN platforms.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Fernández-Berni, Jorge

Rodríguez-Vázquez, Ángel

Carmona-Galán, Ricardo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Eingebettete Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik

Inhaltsverzeichnis

Introduction.- Vision-enabled WSN Nodes: State of the Art.- Processing Primitives for Image Simplification.- VLSI Implementation of Linear Diffusion.- FLIP-Q: A QCIF Resolution Focal-plane Array for Low-power Image Processing.- Wi-FLIP: A Low-power Vision-enabled WSN Node.- Case Study: Early Detection of Forest Fires.