Abstract
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.
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References
Freight Containers-Coding, Identification and marking [ISO 6346 1995(E)]
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Kim, KB., Kim, M., Woo, Y.W. (2007). Recognition of Shipping Container Identifiers Using ART2-Based Quantization and a Refined RBF Network. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_64
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DOI: https://doi.org/10.1007/978-3-540-71629-7_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
Online ISBN: 978-3-540-71629-7
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