Paper
20 April 1995 Bridging the gap between vision and commercial applications
Richard A. Young
Author Affiliations +
Proceedings Volume 2411, Human Vision, Visual Processing, and Digital Display VI; (1995) https://doi.org/10.1117/12.207529
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States
Abstract
There is a large gap between academic vision research and industrial machine vision applications. A cross-fertilization between academic and industrial researchers is of mutual benefit: it leads to practical applications of benefits to industry as well as offering real-world challenges that deepen research understanding. A cross-disciplinary atmosphere between academia and industry should be encourages by appropriate funding sources in government, academia, and industry should be encouraged by appropriate funding sources in government, academia, and industry. Three real-life projects in the author's experience at General Motors demonstrate the potential payoffs of such an approach. An understanding of human hyperacuity mechanisms led to a subpixel method for finding circuit board locations to extremely high accuracy using a low-cost machine vision system. Research on the neural basis of primate color vision led to a proposed new type of color display, based on opponent mechanisms, that should be cheaper than conventional RGB displays. Also, a study of primate motion vision led to machines on the plant floor, and the sensor and display devices needed for vehicles of the 21st century, will greatly benefit from closer interaction between academic and industrial researchers.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard A. Young "Bridging the gap between vision and commercial applications", Proc. SPIE 2411, Human Vision, Visual Processing, and Digital Display VI, (20 April 1995); https://doi.org/10.1117/12.207529
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Cited by 3 scholarly publications.
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KEYWORDS
Machine vision

Visualization

Visual process modeling

Sensors

Cones

Cameras

Eye

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