Crystal surface analysis using matrix textural features classified by a probabilistic neural network

Curry R. Sawyer; Viet Quach; Donald Nason; Lodewijk Van den Berg

doi:10.1117/12.50820

1 December 1991 Crystal surface analysis using matrix textural features classified by a probabilistic neural network

Curry R. Sawyer, Viet Quach, Donald Nason, Lodewijk Van den Berg

Author Affiliations +

Proceedings Volume 1567, Applications of Digital Image Processing XIV; (1991) https://doi.org/10.1117/12.50820
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Curry R. Sawyer, Viet Quach, Donald Nason, and Lodewijk Van den Berg "Crystal surface analysis using matrix textural features classified by a probabilistic neural network", Proc. SPIE 1567, Applications of Digital Image Processing XIV, (1 December 1991); https://doi.org/10.1117/12.50820

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