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MERCURY LEARNING and INFORMATION provides print and digital content in the areas of science and medicine, technology and computing, engineering, and mathematics (STEM disciplines) designed for the professional/reference, trade, library, higher education, career school, and online training markets. We embrace both traditional and innovative publishing models designed to accommodate the needs of the intended audience by using the appropriate delivery methods.
Embedded Vision
An Introduction
ISBN: 9781683924579
Pub Date: October 2019
Specs: 7 x 9 Paperback
Pages: 580
Price: $69.95
Embedded vision is the integration of "computer vision" into machines that use algorithms to decode meaning from observed images or video. It has a wide range of applications to machine learning, artificial intelligence, industrial, medical, driverless cars, drones, smart phones, aerospace, defense, agriculture, consumer, surveillance, robotics, and security. This book is an introductory guide for anyone who is interested in designing machines that have vision-enabled, embedded systems products. It covers a large number of topics encountered in hardware architecture, software algorithms, applications, advancements in camera, processors, and sensors in the field of embedded vision.
Features
- Includes a wide range of applications to artificial intelligence, machine learning, industry, science, medicine, transportation, robotics, and security
- Covers a large number of topics encountered in hardware architecture, software algorithms, applications, and the latest research and advancements in processors and sensors
Brief Table Of Contents
1. Embedded Vision. 2. Industrial Vision. 3. Medical Vision. 4. Video Analytics. 5. Digital Image Processing. 6. Camera-Image Sensors. 7. Embedded Vision Processors and Sensors. 8. Computer Vision. 9. Artificial Intelligence for Embedded Vision. 10. Vision-Based, Real-Time Examples. Appendix: Glossary. Index
ABOUT THE AUTHORS
S. R. Vijayalakshmi, PhD & S. Muruganand, PhD teach courses in wireless communication and engineering and are the authors of Wireless Sensor Networks (Mercury Learning and Information).