Machine vision
Performance improvement of fuzzy RBF networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Performance improvement of RBF network using ART2 algorithm and fuzzy logic system
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
An enhanced ART2 neural network for clustering analysis
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
An automated vision system for container-code recognition
Expert Systems with Applications: An International Journal
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Generally, it is difficult to find constant patterns on identifiers in a container image, since the identifiers are not normalized in color, size, and position, etc. and their shapes are damaged by external environmental factors. This paper distinguishes identifier areas from background noises and removes noises by using an ART2-based quantization method and general morphological information on the identifiers such as color, size, ratio of height to width, and a distance from other identifiers. Individual identifier is extracted by applying the 8-directional contour tracking method to each identifier area. This paper proposes a refined ART2-based RBF network and applies it to the recognition of identifiers. Through experiments with 300 container images, the proposed algorithm showed more improved accuracy of recognizing container identifiers than the others proposed previously, in spite of using shorter training time.