This webcast will show the capabilities of the latest 3D vision equipment, including two sensor platforms, new software tools, and a presentation about capturing 3D scenes in motion
Resources
Many machine vision applications such as OCR/barcode reading on bottles and containers, or defect detection inside threaded bores require inspecting features randomly located both on the part outer or inner sides, and on the top and bottom surfaces. This paper describes the advantages of using special optics designed for 360° inspection (either using a pericentric design or various lens/mirror combinations) versus multi-camera systems or line-scan imaging.
Hybrid AI helps designers and integrators balance the best solution and integration with existing infrastructure as they navigate through Industry 4.0, Internet of Things, and artificial intelligence.
Ruggedised lenses address some of the challenges faced in environments with high levels of vibration, shock, and moisture. Edmund Optics explores the features and advantages of different types of ruggedisation in imaging lenses.
Deep learning is a key enabler of Industry 4.0 in the manufacturing sector where machine vision is an important contributor. This white paper from Matrox Imaging details how and where machine vision benefits from deep learning technology, and how to get the best out of deep learning for machine vision.
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Latest issue
Farmers are starting to reap the rewards of robotics and machine vision, as Keely Portway finds out
Open source software has advanced to a point where it’s now a credible option for industrial imaging, Matthew Dale finds
Chris Beynon, Active Silicon’s CTO and technical chair of the Coaxpress committee, updates on the Coaxpress standard
The Khronos Group and the EMVA are to explore software standards for embedded vision. Khronos’ Neil Trevett and EMVA’s Chris Yates explain the work
Greg Blackman reports from the Embedded World show, where industry experts gave insights into vision processing at the edge
Greg Blackman speaks to Guy Meynants, formerly of Cmosis, and Paul Jerram, of Teledyne e2v, about the history of the image sensors onboard the Mars rover