Trends in Sensors for Embedded Vision

By incorporating acquisition and sensing hardware into the same device, embedded vision promises a reduction in data transfer requirements, power consumption, and size. It is especially suited to machine vision, with embedded vision architecture capturing data and performing in-situ analysis to rapidly inform the actions of other, for example mechanical, systems.

While conventional CMOS image sensors can be used within embedded vision systems, the immediate data processing lends itself to compact sensors that detect information other than a conventional visible image. Examples include event-based vision and miniaturized spectral imaging.

This presentation outlines the principles and benefits of these emerging sensor types and how they fit within embedded vision applications.

About the presenter
Matthew Dyson, Ph.D., is a principal technology analyst at IDTechEx, based in London. He received a master’s and doctorate degree in physics from Imperial College London, in which he investigated organic semiconductors, followed by post-doctoral research at Eindhoven Technical University in the Netherlands researching organic photodetectors. At IDTechEx Matthew utilizes this technical background to assess technical developments and commercial opportunities across a range of emerging electronics technologies.