Abstract
The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. 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 a container image 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 extracted identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm works better on the extraction and recognition of container identifiers compared to conventional algorithms.
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ISO-6346 (1995) Freight containers-coding-identification and marking
Kim NB (1999) Character segmentation from shipping container image using morphological operation. J Korea Multimedia Soc 2(4):390–399
Kim KB (2003) The identifier recognition from shipping container image by using contour tracking and self-generation supervised learning algorithm based on enhanced ART1. J Korean Intell Inf Syst 9(3):65–80
Ramac LC, Varshney PK (1997) Image thresholding based on ali-silvey distance measures. Pattern Recognit 30(7):1161–1173
Marchand-Maillet S, Sharaiha YM (2000) Binary digital image processing. Academic, New York
Kim SG, Kim EK, Kim MH (2003) An enhancement of removing noise branches by detecting noise blobs. J Korea Multimedia Soc 6(3):419–428
Kim KB, Jang SW, Kim CK (2003) Recognition of car license plate by using dynamical thresholding and enhanced neural networks. In: Lecture notes in computer science. LNCS 2756. Springer, Berlin Heidelberg New York, pp 309–319
Carpenter GA, Grossberg S (1992) Neural networks for vision and image processing. Massachusetts Institute of Technology, Cambridge
Kim KB, Kim CK (2004) Performance improvement of RBF network using ART2 algorithm and fuzzy logic system. In: Lecture notes in artificial intelligence. LNAI 3339, pp 853–860
Carpenter GA, Grossberg S, Reynolds JH (1991) ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw 4:565–588
Zimmermann HJ (1991) Fuzzy set theory and it’s applications. Kluwer, Dordrecht
Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3:259–266
Kim KB, Yun HW (1999) A study on recognition of bronchogenic cancer cell image using a new physiological fuzzy neural networks. Jpn J Med Electron Biol Eng 13(5):39–43
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Kim, KB., Cho, JH. Recognition of Identifiers from Shipping Container Images Using Fuzzy Binarization and Enhanced Fuzzy RBF Network. Soft Comput 11, 213–220 (2007). https://doi.org/10.1007/s00500-006-0062-x
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DOI: https://doi.org/10.1007/s00500-006-0062-x