More and more, machine vision systems are expected to make dynamic, automated decisions based on variable conditions. The amount of time and effort to develop these systems can be daunting. Today, the advent of deep learning is changing this landscape and putting automation within the reach of many. Resources such as open-source libraries, Nvidia hardware, and FLIR cameras are helping to make this change happen FLIR cameras have advanced features that minimize the image pre-processing required for neural network training, work seamlessly with platforms such as NVidia Jetson TX-2 and Drive PX 2, and offer 24/7 reliability for trouble-free deployment.
The next generation of high-performance CMOS sensors will open the door to exciting new vision applications, but taking advantage of them will depend on whether your interface can keep up. Ethernet and USB interfaces are increasing their transfer rates to 10Gb/s, adding to the strengths that make them dominant camera interfaces. What are these new technologies and are they ready for machine vision use?
Adding embedded processing to simple sensors can make them 'smart' - but that is just the beginning of the story
Imaging lenses used in many industrial machine vision applications have special requirements beyond those of standard imaging lenses.
This webcast, hosted by Imaging and Machine Vision Europe and sponsored by Lumenera, Bitflow and PCO, explores the broad and significant impact imaging solutions have had within the field of life sciences.
Cognex has strengthened its position in 3D vision with a number of recent company acquisitions in the area, as Greg Blackman discovers
Barry Warzak, owner and founder, Midwest Optical Systems
Embedded processing is opening up a huge market for imaging, a market that machine vision suppliers are trying to tap into. Greg Blackman attended the Embedded Vision Summit in Santa Clara, where Allied Vision launched its new camera platform
Rob Ashwell looks at how vision fits into the battery of sensors onboard autonomous vehicles
The harvesting process could be on the verge of a complete overhaul thanks to machine vision, finds Matthew Dale