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Proceedings Paper

Self-training inspection system for the on-line inspection of printed material
Author(s): Hal E. Beck; Daniel W. McDonald; Dragana P. Brzakovic
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Paper Abstract

The system presented in this paper is a self-training visual inspection system that detects and classifies flaws in digitized images of surfaces with known characteristics. The system is composed of a control unit a signalprocessing unit and aclassifier. The control unitmonitors the generation andplacement of simulated flaws learning schedules and provides the teaching signal to the classifier. The signal processing unit simulates an optical area-to-line transformation for high speed processing and extracts regions of interest. The classifier is a multi-layer connectionist neural network. Two inspection tasks are targeted and the system''s performance in each is analyzed in terms of the neural network''s behavior including various learning schedules and application of three diagnostic tools developed in this work. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21199
Show Author Affiliations
Hal E. Beck, Martin Marietta Corp. (United States)
Daniel W. McDonald, Oak Ridge National Lab. (United States)
Dragana P. Brzakovic, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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