Biological systems are a source of inspiration in the development of small autonomous sensor nodes. The two major types of optical vision systems found in nature are the single aperture human eye and the compound eye of insects. The latter are among the most compact and smallest vision sensors. The eye is a compound of individual lenses with their own photoreceptor arrays. The visual system of insects allows them to fly with a limited intelligence and brain processing power. A CMOS image sensor replicating the perception of vision in insects is discussed and designed in this book for industrial (machine vision) and medical applications. The CMOS metal layer is used to create an embedded micro-polarizer able to sense polarization information. This polarization information is shown to be useful in applications like real time material classification and autonomous agent navigation. Further the sensor is equipped with in pixel analog and digital memories which allow variation of the dynamic range and in-pixel binarization in real time. The binary output of the pixel tries to replicate the flickering effect of the insect’s eye to detect smallest possible motion based on the change in state. An inbuilt counter counts the changes in states for each row to estimate the direction of the motion. The chip consists of an array of 128x128 pixels, it occupies an area of 5 x 4 mm2 and it has been designed and fabricated in an 180nm CMOS CIS process from UMC.

As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.

The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material on development of computers for gaining understanding about videos and digital images. Highlighting a range of topics, such as computational models, machine learning, and image processing, this multi-volume book is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.

The field of mechatronics integrates modern engineering science and technologies with new ways of thinking, enhancing the design of products and manufacturing processes. This synergy enables the creation and evolution of new intelligent human-oriented machines. The Handbook of Research on Advancements in Robotics and Mechatronics presents new findings, practices, technological innovations, and theoretical perspectives on the the latest advancements in the field of mechanical engineering. This book is of great use to engineers and scientists, students, researchers, and practitioners looking to develop autonomous and smart products and systems for meeting today’s challenges.

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

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.

"This book provides interdisciplinary research that evaluates the performance of machine visual models and systems in comparison to biological systems, blending the ideas of current scientific knowledge and biological vision"--

This three volume set, CCIS 771, 772, 773, constitutes the refereed proceedings of the CCF Chinese Conference on Computer Vision, CCCV 2017, held in Tianjin, China, in October 2017. The total of 174 revised full papers presented in three volumes were carefully reviewed and selected from 465 submissions. The papers are organized in the following topical sections: biological vision inspired visual method; biomedical image analysis; computer vision applications; deep neural network; face and posture analysis; image and video retrieval; image color and texture; image composition; image quality assessment and analysis; image restoration; image segmentation and classification; image-based modeling; object detection and classification; object identification; photography and video; robot vision; shape representation and matching; statistical methods and learning; video analysis and event recognition; visual salient detection.

Smart sensors and MEMS can include a variety of devices and systems that have a high level of functionality. They do this either by integrating multiple sensing and actuating modes into one device, or else by integrating sensing and actuating with information processing, analog-to-digital conversion and memory functions.Part one outlines the industrial applications for smart sensors, covering direct interface circuits for sensors, capacitive sensors for displacement measurement in the sub-nanometer range, integrated inductive displacement sensors for harsh industrial environments, advanced silicon radiation detectors in the vacuum ultraviolet (VUV) and extreme ultraviolet (EUV) spectral range, and advanced optical incremental sensors (encoders and interferometers), among other topics. The second part of the book describes the industrial applications of smart micro-electro-mechanical systems (MEMS). Some of the topics covered in this section include microfabrication technologies used for creating smart devices for industrial applications, microactuators, dynamic behaviour of smart MEMS in industrial applications, MEMS integrating motion and displacement sensors, MEMS print heads for industrial printing, Photovoltaic and fuel cells in power MEMS for smart energy management, and radio frequency (RF)-MEMS for smart communication microsystems.Smart sensors and MEMS is invaluable reference for academics, materials scientists and electrical engineers working in the microelectronics, sensors and micromechanics industry, and engineers looking for industrial sensing, monitoring and automation solutions. - Outlines industrial applications for smart sensors and smart MEMS - Covers smart sensors including capacitive, inductive, resistive and magnetic sensors and sensors to detect radiation and measure temperature - Covers smart MEMS including power MEMS, radio frequency MEMS, optical MEMS, inertial MEMS, and microreaction chambers