This chapter presents a set of introductory material, which in addition to providing a general view on the topic, highlights the importance of research in this area. It also presents a short history of the design of smart vision sensors, and points out some of the fundamental issues in the design of such sensors. 1. 1 A General Overview Machine vision is one of the main branches of artificial intelligence. The richness of information present in images makes them the first choice as an input to an artificial system which tries to interact with its environment. A large proportion of the brain of many advanced species is dedicated to visual information processing, which illustrates the importance of visual information in biological systems. Biological visual systems have evolved over millions of years, and each specie has developed a specialized visual system tailored for the essential tasks of survival, such as catching a prey, or escaping a predator. Implementing electronic hardware for image processing, therefore, may benefit from the underlying fundamental aspects of biological vision, though in no respect should this be regarded as a solid framework for electronic vision systems. Traditionally, computer vision algorithms are performed on images captured by conventional cameras, and processing is accomplished by means of general purpose digital computers. More advanced systems utilize dedicated hardware to speed up the processing stage.

The 11 th IFIP International Conference on Very Large Scale Integration, in Montpellier, France, December 3-5,2001, was a great success. The main focus was about IP Cores, Circuits and System Designs & Applications as well as SOC Design Methods and CAD. This book contains the best papers (39 among 70) that have been presented during the conference. Those papers deal with all aspects of importance for the design of the current and future integrated systems. System on Chip (SOC) design is today a big challenge for designers, as a SOC may contain very different blocks, such as microcontrollers, DSPs, memories including embedded DRAM, analog, FPGA, RF front-ends for wireless communications and integrated sensors. The complete design of such chips, in very deep submicron technologies down to 0.13 mm, with several hundreds of millions of transistors, supplied at less than 1 Volt, is a very challenging task if design, verification, debug and industrial test are considered. The microelectronic revolution is fascinating; 55 years ago, in late 1947, the transistor was invented, and everybody knows that it was by William Shockley, John Bardeen and Walter H. Brattein, Bell Telephone Laboratories, which received the Nobel Prize in Physics in 1956. Probably, everybody thinks that it was recognized immediately as a major invention.

High Performance Silicon Imaging: Fundamentals and Applications of CMOS and CCD Sensors, Second Edition, covers the fundamentals of silicon image sensors, addressing existing performance issues and current and emerging solutions. Silicon imaging is a fast growing area of the semiconductor industry. Its use in cell phone cameras is already well established, with emerging applications including web, security, automotive and digital cinema cameras. The book has been revised to reflect the latest state-of-the art developments in the field, including 3D imaging, advances in achieving lower signal noise, and new applications for consumer markets. The fundamentals section has also been expanded to include a chapter on the characterization and testing of CMOS and CCD sensors that is crucial to the success of new applications. This book is an excellent resource for both academics and engineers working in the optics, photonics, semiconductor and electronics industries. - Covers the fundamentals of silicon-based image sensors and technical advances, focusing on performance issues - Looks at image sensors in applications, such as mobile phones, scientific imaging, and TV broadcasting, and in automotive, consumer and biomedical applications - Addresses the theory behind 3D imaging and 3D sensor development, including challenges and opportunities

Revised and expanded for this new edition, Smart CMOS Image Sensors and Applications, Second Edition is the only book available devoted to smart CMOS image sensors and applications. The book describes the fundamentals of CMOS image sensors and optoelectronic device physics, and introduces typical CMOS image sensor structures, such as the active pixel sensor (APS). Also included are the functions and materials of smart CMOS image sensors and present examples of smart imaging. Various applications of smart CMOS image sensors are also discussed. Several appendices supply a range of information on constants, illuminance, MOSFET characteristics, and optical resolution. Expansion of smart materials, smart imaging and applications, including biotechnology and optical wireless communication, are included. Features • Covers the fundamentals and applications including smart materials, smart imaging, and various applications • Includes comprehensive references • Discusses a wide variety of applications of smart CMOS image sensors including biotechnology and optical wireless communication • Revised and expanded to include the state of the art of smart image sensors

Presents recent technical information and gives an overview of progress in optical memory, neural networks and fractals from the viewpoint of optical information processing. The work introduces holographic optical disks and holographic storage in photorefractive crystal fibre, discusses the optical implementation of neural networks, explains the use of neurochips as artificial retinas, and more.

This book presents recent advances in image sensing and processing systems, image recognition, 3D imaging and processing, ultrafast optical networks for image communication, and multidimensional information security systems. Eleven chapters by international experts provide practical and theoretical insights. Useful for students, researchers, and technology users in IT, image processing, and optics.

This book provides a detailed introduction to near-sensor and in-sensor computing paradigms, their working mechanisms, development trends and future directions. The authors also provide a comprehensive review of current progress in this area, analyze existing challenges in the field, and offer possible solutions. Readers will benefit from the discussion of computing approaches that intervene in the vicinity of or inside sensory networks to help process data more efficiently, decreasing power consumption and reducing the transfer of redundant data between sensing and processing units. Provides readers with a detailed introduction to the near-sensor and in-sensor computing paradigms; Includes in-depth and comprehensive summaries of the state-of-the-art development in this field; Discusses and compares various neuromorphic sensors and neural networks: Describes integration technology for near-/in-sensor computing; Reveals the relationship between near-/in-sensor computing and other computing paradigms, such as neuromorphic computing, edge computing, intuitive computing, and in-memory computing.