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Cover of Vision Chips

a novel ·

Vision Chips

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Vision chips, or smart visual sensors, are those sensors that have integrated image acquisition and parallel processing, often at the pixel level, using dedicated analog and digital circuits. Vision Chips presents a systematic approach to the design and analysis of …

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the long version

Vision chips, or smart visual sensors, are those sensors that have integrated image acquisition and parallel processing, often at the pixel level, using dedicated analog and digital circuits. Vision Chips presents a systematic approach to the design and analysis of vision chips using analog VLSL. It presents algorithmic level implementation issues, from both the VLSI and computer vision points of view. It reviews the VLSI technologies and general analog VLSI design methodologies, in the context of suitability for vision chips. It describes chip-level architectural issues, including tessellation structures, pixel-processor interaction, and data read-out. It presents detailed analysis of building-blocks necessary in vision chips, including photodetectors, photocircuits, and spatial and temporal processing circuits. It addresses other important design issues, such as testing, digital noise, and mismatch. In addition Vision Chips reviews some of the past and existing implementations of smart vision sensors. It contains condensed information on more than fifty vision chips, designed by research laboratories all over the world. Novel and interesting features of each vision chip have been highlighted through informative diagrams and concise descriptions. This book is a valuable asset for researchers in the area, engineers working on the design of vision sensors, graduate students working in analog VLSI and vision, and computer vision and biological vision researchers and scientists.

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"Vision chips, or smart visual sensors, are those sensors that have integrated image acquisition and parallel processing, often at the pixel level, using dedicated analog and digital circuits. Vision Chips …"

— Margaret

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