The Key of Bulk Warehouse Grain Quantity Recognition

Rectangular Benchmark Image Recognition
  • Ying Lin
  • Yang Fu
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 258)

According to requests of bulk warehouse grain quantity recognition, we take the scene video as identified object to obtain the object’s boundary from the result of edge detection difference iterative analysis. By using region iterative threshold value of gradient operator fitted closely with identified target carries to execute the picture characteristic second-extract and then to carrying on rectangular benchmark judgment using the membership functions of fuzzy recognition, we adopt the Visual C++ realized this recognition algorithm. And the experimental results show that this recognition algorithm effectively enhances the anti-jamming, robustness and the recognition precision and effect.


edge detection difference fuzzy recognition membership functions iterative analysis 


  1. Canny, J. (1986). A Computational Approach to Edge Detection [J], IEEE Transations Pattern Analytical Machine Intelligent 8: 679-698.CrossRefGoogle Scholar
  2. Cheng Xiao-chun (2006). A method of shape recognition [J], Pattern Recognition and Artificial Intelligence 6: 126-132.Google Scholar
  3. Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis [J], New York: Wiley.Google Scholar
  4. Fu, K. S. & Mui, J. K. (1981). A Survey of Image Segmentation. Pattern Recognition [J], IEEE Transactions on Pattern Analysis and Machine Intelligence 13(1): 3-16.Google Scholar
  5. Gao, J., Zhou, M. & Wang, H. (2001). A Threshold and Region Growing Combined Method for Filament Disappearance Area Detection in Solar Images [J], In Proceedings of The Conference on Information Sciences and Systems. The John Hopkins University.Google Scholar
  6. Huertas, A. & Medioni G. (1986). Detection of Intensity Changes with Sub-Pixel Accuracy using Laplacian-Gaussian Masks [J], IEEE Transactions Pattern Recognition Machine Intelligent 8(5): 651-664.CrossRefGoogle Scholar
  7. J. F. Canny (Nov 1986), A Computational Approach to Edge Detection [J], IEEE PAMI, Vol. 8, No. 6.Google Scholar
  8. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas. (1998). On combining classifiers [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3): 226-239.CrossRefGoogle Scholar
  9. Kim, V. & L. Yaroslavskii, (1986). Rank algorithms for picture processing [J], Comput Vision, Graphics, and Image Process 35: 234-258.CrossRefGoogle Scholar
  10. M.D. Kelly, Edge Detection in Pictures by Computer Planning [J], Machine Intelligence,Vol. 6 (American Elsevier, New York, 1973), pp. 397-409.Google Scholar
  11. Marr, D. & Hildreth, E. (1980). Theory of Edge Detection [J], Proceedings of the Royal Society London B207: 187-217.CrossRefGoogle Scholar
  12. Pursuing technology. (2006). Visual C++ digital image disposal typical arithmetic and implement [M]. post & telecom press.Google Scholar
  13. Pavlidis, T. (1982). Algorithms for Graphics and Image Processing [J], Computer Science Press, Maryland, USA.CrossRefGoogle Scholar
  14. T. Poggio, H. Voorhees and A. Yuille. (1985). A Regularized Solution to Edge Detection [J], May 1985 A. I. Memo 883, M.I.T.Google Scholar
  15. Tou, J.T. & R.C. Gonzalez.Pattern Recognition Principles [M], Addison-Wesley Publishing, Reading, MA, USA, 1981.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Ying Lin
    • 1
  • Yang Fu
    • 1
  1. 1.School of ManagementChongqing Jiao Tong UniversityChina

Personalised recommendations