The Research on Grain Reserve Intelligent Audit Method and Implementation in Three-dimensional Stores

  • Ying Lin
  • Xiaohui Jiang
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 258)

In order to eliminate the drawbacks in grain reserve management, a grain reserve intelligent audit method was designed. It was achieved by using the edge detection technology towards image samples to determine the edge of each object in the photograph, including the grain, the wall and benchmarks, and then separately holding pattern recognition to ascertain the identity of each object. Then according to the basic theories of 3-D reconstruction technology, combining with the location information of these objects in the original photographs, the underlying quantitative information could be dug out. At last, using grain weight measuring algorithm, which had been combined with the perspective error correction method, achieve the real-time and precise audit to the grain in stock and each installment.


Grain reserve Intelligent audit Image recognition Perspective error 


  1. Haven, Betty H. (1977). A Method for Minimizing Perspective Error. Journal of Physical Education and Recreation. 48(4), 74-77.Google Scholar
  2. Wei Yong-lu, Zhang Ji-yue, Ji Wen-gang. (2005). Design of distributed control system for grain depot based on MODBUS/TCP Protocol. Beijing Institute of Petrochemical Technology Journal. Chn, 13(2): 2-4.Google Scholar
  3. Li Jian-hua, Sun Hai-bo, Liu Zhan-liang, et al. (2003). The design and realization of supervising on provisions situation system [J]. Journal of the Hebei Academy of Sciences, 3(4): 224-227.Google Scholar
  4. Qiang Wei-zhe, Song Guang-hua, Zheng Yao. (2005). 3D reconstruction based on image segmentation [J]. Computer Engineering and Application, 36: 77-82.Google Scholar
  5. Liu li-bo. (2001). Summarization of segmental way of imagine. Ningxia Agricultural College Journal. Chn, 4(22), 51-53.Google Scholar
  6. Canny, J. (1986). A Computational Approach to Edge Detection [J], IEEE Transations Pattern Analytical Machine Intelligent 8: 679-698.CrossRefGoogle Scholar
  7. D. Demigny. (2002). On Optimal Linear Filtering for Edge Detection, IEEE Trans. Image Processing, 2002, 9(11): 728-737.CrossRefGoogle Scholar
  8. Cheng Xiao-chun. (2006). A method of shape recognition [J]. Pattern Recognition and Artificial Intelligence, 6: 126-132.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Ying Lin
    • 1
  • Xiaohui Jiang
    • 1
  1. 1.School of ManagementChongqing Jiao Tong UniversityChina

Personalised recommendations