An Intelligent System for Container Image Recognition Using ART2-Based Self-organizing Supervised Learning Algorithm

  • Kwang-Baek Kim
  • Young Woon Woo
  • Hwang-Kyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is black or white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Noise areas are replaced with a mean pixel value of the whole image and areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tracking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which creates nodes of the hidden layer by applying ART2 between the input and the hidden layers and improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm between the hidden and the output layers. Experiments using many images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.


Hide Layer Edge Detection Shipping Container Fuzzy Method Binarization Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwang-Baek Kim
    • 1
  • Young Woon Woo
    • 2
  • Hwang-Kyu Yang
    • 3
  1. 1.Dept. of Computer EngineeringSilla UniversityBusanKorea
  2. 2.Dept. of Multimedia EngineeringDong-Eui UniversityBusanKorea
  3. 3.Dept. of Multimedia EngineeringDongseo UniversityBusanKorea

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