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Proceedings Paper

Crystal surface analysis using matrix textural features classified by a probabilistic neural network
Author(s): Curry R. Sawyer; Viet Quach; Donald Nason; Lodewijk Van den Berg
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Paper Abstract

A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlapping sub-images and features are extracted from each sub-image based on statistical measures of the gray tone distribution, according to the method of Haralick. Twenty parameters are derived from each sub-image and presented to a probabilistic neural network (PNN) for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities.

Paper Details

Date Published: 1 December 1991
PDF: 10 pages
Proc. SPIE 1567, Applications of Digital Image Processing XIV, (1 December 1991); doi: 10.1117/12.50820
Show Author Affiliations
Curry R. Sawyer, EG&G Energy Measurements, Inc. (United States)
Viet Quach, EG&G Energy Measurements, Inc. (United States)
U.S. Dept. of Energy/Nevada Operations Office (United States)
Donald Nason, EG&G Energy Measurements, Inc. (United States)
Lodewijk Van den Berg, EG&G Energy Measurements, Inc. (United States)
U.S. Dept. of Energy/Nevada Operations Office (United States)


Published in SPIE Proceedings Vol. 1567:
Applications of Digital Image Processing XIV
Andrew G. Tescher, Editor(s)

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