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1 August 1990 Self-training inspection system for the on-line inspection of printed material
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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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hal E. Beck, Daniel W. McDonald, and Dragana P. Brzakovic "Self-training inspection system for the on-line inspection of printed material", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21199
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