The phrase "machine vision technology" is correct and usable in written English.
It is typically used to refer to the field of technology that involves the use of electronic devices to capture and analyze images and data for various applications. Example: Advancements in machine vision technology have greatly improved the quality control process in manufacturing industries.
Currently much commercial machine vision technology is done by having humans "supervise" the learning process by labeling specific features.
The technique transformed the field of artificial intelligence and also greatly advanced voice recognition and machine vision technology.
That machine vision technology is also the basis of the self-driving car efforts from Google's sister firm Waymo.
Skeptics have pointed to Soon-Shiong's past hostility toward publications critical of his businesses, and to slightly bewildering comments he made, to Bloomberg News, in 2016, about using "machine vision" technology to revolutionize journalism.
Ford also said it had made investments in Nirenberg Neuroscience, which is also developing machine vision technology, and Civil Maps, a start-up that is developing 3D digital maps for use by automated vehicles.
Machine vision technology now offers a viable means of monitoring and control of the flotation circuits.
Agricultural engineering technologies have successfully addressed certain challenges by the use of advanced sensors and machine vision technologies.
"Intel offered serious money" to settle a claim on machine-vision technology used in such things as bar-coding and robotics, says the attorney.
This makes plenty of sense – the special technology associated with Kinnect comes out of Israeli 'Machine Vision' engineering, and guess where Machine Vision is a speciality?
She has multidisciplinary research experiences including machine vision measurement technology, optical metrology, and biomedical detection technology.
We explore and report information needed to automatically download and assemble Google Street View images, as well as other image parameters for a wide range of analysis and visualization, and explore extracting pedestrian count data based on these images using machine vision and learning technology.
