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22 January 1987 Adaptation Of A Parallel Architecture Computer To Phase Shifted Moire Interferometry
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Proceedings Volume 0728, Optics, Illumination, and Image Sensing for Machine Vision; (1987)
Event: Cambridge Symposium_Intelligent Robotics Systems, 1986, Cambridge, MA, United States
Even though moire interferometry techniques have been around for some time now, they have not been implemented in very many machine vision applications. To date, the main draw back of moire interferometry has been the difficulty associated with the processing of the image. The processing time can range from several minutes to hours. This paper will concern itself with work to reduce the processing time to near frame rate speeds by using a vision flow type architecture vision system. Phase shifted moire is a full-field technique of mapping three dimensional information into two dimensional space. Unlike conventional moire interferometry, there is no ambiguity as to the sign of the z (depth) coordinate and there are no fringe centers to be located or fringe mapping to be done. Phase shifting naturally filters out noise in the image, and can even provide contrast information that can help determine if the data at a given point is good. Being a full-field technique, phase shifted moire interferometry produces a data rich set of three dimensional coordinates for all points on an object surface. By its nature, full-field data analysis is necessarily a computational intensive task. Traditionally, several images are acquired and stored away to later be processed in a serial fashion. By capturing and processing the images with a massively parallel vision engine, the total time required to process the data can be reduced significantly.
© (1987) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Albert J. Boehnlein and Kevin G. Harding "Adaptation Of A Parallel Architecture Computer To Phase Shifted Moire Interferometry", Proc. SPIE 0728, Optics, Illumination, and Image Sensing for Machine Vision, (22 January 1987);


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