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2017 | 24 | Special Issue S3 |
Tytuł artykułu

A positioning lockholes of container corner castings method based on image recognition

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article proposes a method of locating and recognizing lockholes in shipping container corner castings. This method converts the original image of the containers captured by a camera into the HSV (Hue, Saturation, Value) color space. To reduce the influence of the surface color of the containers and lights from the environment on the locating and recognizing algorithm, most noisy points of the image are filtered by binarization and a morphology opening operation to make the features of the containers clearer in the image. Thus, the container body can be separated from the total image. Then, the position and size of the corner castings are defined through calculation based on the international standard of the shipping container size. Lastly, by using this method, we can locate the corner casting in the image by using the General Hough Transform fitting algorithm onto ellipses
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
24
Opis fizyczny
p.95-101,fig.,ref.
Twórcy
autor
  • Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Higher Technical College, Shanghai Maritime University, Shanghai, China
autor
  • Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, Shanghai, China
autor
  • Shanghai Weiguo Port Equipment Company Limited, Shanghai, China
Bibliografia
  • 1. Lam Jasmine Siu Lee, Song Dong-Wook, “Seaport network performance measurement in the context of global freight supply chains”, Polish Maritime Research, Vol. 20, SI. 1, pp. 47-54 (2013)
  • 2. Gamal Abd El-Nasser A. Said, El-Sayed M. El-Horbaty, “An intelligent optimization approach for storage space allocation at seaports: A case study”, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 66-72 (2015)
  • 3. Song Su, “Ship emissions inventory, social cost and ecoefficiency in Shanghai Yangshan port”, ATMOSPHERIC ENVIRONMENT, Vol. 82, pp. 288-297 (2014)
  • 4. Mi Chao, Huang Youfang, Liu Haiwei, Shen Yang, Mi Weijian. “Study on Target Detection & Recognition Using Laser 3D Vision System for Automatic Ship Loader,” Sensor & Transducers, Vol. 158, No.11, pp. 436-442 (2013)
  • 5. Yun Xie, Qifan Bao, Zhenqiang Yao, Zhongxiong Ge, Zhengchun Du, “First automatic empty container yard with no operator in China”, Technology and Innovation Conference, 2006. ITIC 2006. International, pp. 1509-1513 (2006)
  • 6. Mi Chao, Shen Yang, Mi Weijian, Huang Youfang, “Ship Identification Algorithm Based on 3D Point Cloud for Automated Ship Loaders”, Journal of Coastal Research, SI.73, pp.28-34 (2015)
  • 7. Mi Chao, Zhang Zhiwei, He Xin, Huang Youfang, Mi Weijian, “Two-stage classification approach for human detection in camera video in bulk ports”, POLISH MARITIME RESEARCH, Vol. 22, pp. 163-170 (2015)
  • 8. Mi Chao, He Xin, Liu Haiwei, Huang Youfang, Mi Weijian, “Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports”, MATHEMATICAL PROBLEMS IN ENGINEERING (2015)
  • 9. M. Goccia, M. Bruzzo, C. Scagliola, S. Dellepiane, “Recognition of container code characters through graylevel feature extraction and gradient-based classifier optimization”, Document Analysis and Recognition, pp. 973-977 (2003)
  • 10. Mi Chao, Zhang Zhiwei, Huang Youfang, Shen Yang, “A Fast Automated Vision System for Container Corner Casting Recognition”, Journal of Marine Science and Technology - Taiwan, Vol.24, No.1, pp.54-60 (2016)
  • 11. Chen Mo, Wu Wei, Yang Xiaomin, He Xiaohai, “HiddenMarkov-Model-Based Segmentation Confidence Applied to Container Code Character Extraction”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 12, No. 4, pp. 11471156 (2011)
  • 12. Kumano S., Miyamoto K., Tamagawa M., et al., “Development of a container identification mark recognition system”, ELECTRONICS AND COMMUNICATIONS IN JAPAN PART II-ELECTRONICS, Vol. 87, No. 12, pp. 38-50 (2004)
  • 13. Abbate, Stefano, Avvenuti, Marco, Corsini, Paolo, et al., “An Integer Linear Programming Approach for Radio-Based Localization of Shipping Containers in the Presence of Incomplete Proximity Information”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 13, No. 3, pp. 14041419 (2012)
  • 14. Wu Wei, Liu Zheng, Chen Mo, “A New Framework for Container Code Recognition by Using SegmentationBased and HMM-Based Approaches”, INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, Vol. 29, No. 1, 2015
  • 15. Yamashita Yukihiko, Wakahara Toru, “Affinetransformation and 2D-projection invariant k-NN classification of handwritten characters via a new matching measure”, PATTERN RECOGNITION, Vol. 52, pp. 459470 (2016)
  • 16. Alzati Alberto, Carlos Sierra Jose, “Special birational transformations of projective spaces”, ADVANCES IN MATHEMATICS, Vol. 289, pp. 567-602 (2016)
  • 17. Silva A. S., Severgnini F. M. Q., Oliveira M. L., et al., “Object Tracking by Color and Active Contour Models Segmentation”, IEEE LATIN AMERICA TRANSACTIONS, Vol. 14, No. 3, pp. 1488-1493 (2016)
  • 18. Pezeshk Aria, Tutwiler Richard L., “Automatic Feature Extraction and Text Recognition From Scanned Topographic Maps”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 49, No. 12, pp. 5047-5063 (2011)
  • 19. Kim H., Johnson JT., “Radar images of rough surface scattering: Comparison of numerical and analytical models”, IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, Vol. 50, No. 2, pp. 94-100 (2002)
  • 20. Yasmin Jaseema, Sathik Mohamed, “An Improved Iterative Segmentation Algorithm using Canny Edge Detector for Skin Lesion Border Detection”, INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, Vol. 12, No. 4, pp. 325-332 (2015)
  • 21. Lu Tingting, Hu Weiduo, Liu Chang , “Effective ellipse detector with polygonal curve and likelihood ratio test”, IET COMPUTER VISION, Vol. 9, No. 6, pp. 914-925 (2016)
  • 22. Fornaciari Michele, Prati Andrea, Cucchiara Rita, “A fast and effective ellipse detector for embedded vision applications”, PATTERN RECOGNITION, Vol. 47, No. 11, pp. 3693-3708 (2014)
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-f0d7fd9c-723b-493b-bc11-08402ad5c934
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