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.
Machine vision systems have moved beyond elevated sensors to inspection integrated in controllers. Choosing the right based PC-based automation platform can make them even better.
MIT researchers have found similarities between how some computer-vision systems process images and how humans see out of the corners of their eyes.
CoaXPress 2.0 (CXP 2.0) and 10GigE Vision (10 GigE) are the two most popular interfaces for machine vision and discrete sensors; CXP 2.0 has advantages in flexibility, speed, bandwidth and cable length.
An ultra-compact angle sensor built from flat optics captures these measurements at 30 frames per second, which will allow for more accurate and precise measurements of tiny atomic materials.
Manufacturers can benefit from AI machine vision technologies by increasing uptime, leverage preventive maintenance and more.