Abstract
In this paper, we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers, which are the target for recognition. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of individual codes. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm performs better for extraction and recognition of container identifiers compared to conventional algorithms.
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References
ISO-6346.: Freight Containers-Coding-Identification and Marking (1995)
Kim, K.B.: The Identifier Recognition from Shipping Container Image by using Contour Tracking and Self-Generation Supervised Learning Algorithm Based on Enhanced ART1. Journal of Intelligent Information Systems 9(3), 65–80 (2003)
Ramac, L.C., Varshney, P.K.: Image Thresholding Based on Ali-Silvey distance Measures. Pattern Recognition 30(7), 1161–1173 (1997)
Kim, K.B., Jang, S.W., Kim, C.K.: Recognition of Car License Plate by Using Dynamical Thresholding and Enhanced Neural Networks. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 309–319. Springer, Heidelberg (2003)
Carpenter, G.A., Grossberg, S.: Neural Networks for Vision and Image Processing. Massachusetts Institute of Technology (1992)
Kim, K.B., Kim, C.K.: Performance Improvement of RBF Network using ART2 Algorithm and Fuzzy Logic System. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 853–860. Springer, Heidelberg (2004)
Zimmermann, H.J.: Fuzzy set Theory and it’s Applications. Kluwer Academic Publishers, Dordrecht (1991)
Kim, K.B., Joo, Y.h., Cho, J.H.: An Enhanced Fuzzy Neural Network. In: Liew, K.-M., Shen, H., See, S., Cai, W. (eds.) PDCAT 2004. LNCS, vol. 3320, pp. 176–179. Springer, Heidelberg (2004)
Kim, K.B., Yun, H.W.: A Study on Recognition of Bronchogenic Cancer Cell Image using A New Physiological Fuzzy Neural Networks. Japanese Journal of Medical Electronics and Biological Engineering 13(5), 39–43 (1999)
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Kim, KB. (2005). Recognition of Identifiers from Shipping Container Images Using Fuzzy Binarization and Enhanced Fuzzy Neural Network. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_95
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DOI: https://doi.org/10.1007/11539506_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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