A labeled object identification system using multilevel neural networks

Abstract

This paper describes the design of a neural network based labeled object identification system, to be used for product classification at the final inspection stage of an IBM personal computer manufacturing line. The objective was to design and identification system using existing equipment that would provide robust and accurate classification, as well as a simple means for adding new product models to the system. In the first stage of the identification system, an image of the product is obtained, and the region containing the label is segmented from the rest of the image. Preprocessing operations are performed to extract the region of interest from the segmented image. Normalized and preprocessed images of the labels are compressed using a fully-connected back-propagation autoencoder network. Features extracted in this manner are used as inputs to a Learning Vector Quantization (LVQ) network, trained to classify the labels. The system so designed is shown to satisfy the primary requirements of a typical industrial classification system.