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Proceedings Paper

Locating Industrial Parts with Subpixel Accuracies
Author(s): Richard A. Young
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Paper Abstract

Industrial machine vision requires speed, accuracy, and low cost. The SUPERSIGHT vision system at General Motors Research locates industrial parts with high accuracy even with low-resolution video cameras. This leads to large savings in the hardware costs of subsequent image processing electronics, and considerable speed improvements over current methods. With a prototype system employing a Reticon 100x100 photodiode array camera, the relative locations of lines, circles, and ellipses in a plane could be routinely determined to 1/10 to 1/20 pixel accuracy, with 1/50 pixel (1 part in 5000) accuracy best case. Theory indicates that there is no upper bound on the accuracy that can be achieved, with the only limiting factor being noise. Primary sources of noise were hypothesized to be fixed-pattern camera noise for within-frame location measurements, and digitization synchronizing error (frame jitter) and mechanical vibration for between-frame measurements. Gaussian digital filters, pixel-by-pixel correction of gain and bias variations, and higher quality solid-state video cameras can reduce the influence of camera fixed-pattern noise. Frame averaging and improved digitizers can reduce frame jitter. In extensions to 3-D surface and edge analysis, multiple-order Gaussian-derivative filters allowed for an efficient means of determining the regression coefficients useful for specifying the 3-D surface shapes of objects from shading, range or stereo data. The Krawtchouck polynomials, the discrete form of the Gaussian derivative functions, allowed more accurate estimates than current facet models of the properties of surface patches, and the locations of edges. Such Gaussian derivative-like operators are similar to the vision operators used by the primate brain, as measured neurophysiologically.

Paper Details

Date Published: 22 January 1987
PDF: 8 pages
Proc. SPIE 0728, Optics, Illumination, and Image Sensing for Machine Vision, (22 January 1987); doi: 10.1117/12.937817
Show Author Affiliations
Richard A. Young, General Motors Research Laboratories (United States)


Published in SPIE Proceedings Vol. 0728:
Optics, Illumination, and Image Sensing for Machine Vision
Donald J. Svetkoff, Editor(s)

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