Efficiency, flexibility and visibility can be improved in manufacturing plants by using sensor-level networks to enable sensors in robots to collect better data and more of it.
MIT researchers have repurposed a 19th century photography technique to make stretchy, color-changing films, which could improve manufacturing of pressure-monitoring bandages, shade-shifting fabrics touch-sensing robots and more.
Penn State researchers examined the ways to decouple input signals for multimodal sensors, which is important for avoiding complicated signal processing steps.
Machine vision and automation advancements are improving logistics and warehousing operations by taking advantage of developments with autonomous mobile robots (AMRs), deep learning and more.
Rice University researchers have developed strain-sensing smart skin that uses very small structures, carbon nanotubes, to monitor and detect damage in large structures.
A Mizzou Engineering team has devised a new way to turn single panoramic images into 3D models with a system called OmniFusion.
An algorithm has been created to solve one of the hardest tasks in computer vision: assigning a label to every pixel without human supervision.
Stanford researchers devised a compact optical device that could soon be used by common digital cameras to measure the distance to objects.
Cornell engineers have created a deep-ultraviolet laser using semiconductor materials that show great promise for improving the use of ultraviolet light for sterilizing medical tools, purifying water, sensing hazardous gases and more.
From optics and lighting to smart cameras to artificial intelligence (AI) and machine learning (ML), machine vision is growing in industrial automation and changing in many ways. Four innovations are highlighted.