Comparison of Image Conversions Between Square Structure and Hexagonal Structure

  • Xiangjian He
  • Jianmin Li
  • Tom Hintz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)


Hexagonal image structure is a relatively new and powerful approach to intelligent vision system. The geometrical arrangement of pixels in this structure can be described as a collection of hexagonal pixels. However, all the existing hardware for capturing image and for displaying image are produced based on rectangular architecture. Therefore, it becomes important to find a proper software approach to mimic hexagonal structure so that images represented on the traditional square structure can be smoothly converted from or to the images on hexagonal structure. For accurate image processing, it is critical to best maintain the image resolution after image conversion. In this paper, we present various algorithms for image conversion between the two image structures. The performance of these algorithms will be compared though experimental results.


Light Intensity Interpolation Method Hexagonal Structure Bilinear Interpolation Lena Image 
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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  1. 1.
    Horn, B.K.P.: Robot Vision. MIT Press, Cambridge, MA & McGraw-Hill, New York (1986)Google Scholar
  2. 2.
    Staunton, R.: The Design of Hexagonal Sampling Structures for Image Digitization and Their Use with Local Operators. Image and Vision Computing 7(3), 162–166 (1989)CrossRefGoogle Scholar
  3. 3.
    Wuthrich, C.A., Stucki, P.: An Algorithmic Comparison between Square- and Hexagonal-based Grids. CVGIP: Graphical Models and Image Processing 53(4), 324–339 (1991)CrossRefGoogle Scholar
  4. 4.
    He, X.: 2-D Object Recognition with Spiral Architecture. PhD Thesis. University of Technology, Sydney (1999)Google Scholar
  5. 5.
    Wu, Q., He, X., Hintz, T.: Virtual Spiral Architecture. In: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, vol. 1, pp. 399–405 (2004)Google Scholar
  6. 6.
    Wang, H., Wang, M., Hintz, T., He, X., Wu, Q.: Fractal Image Compression on a Pseudo Spiral Architecture. Australian Computer Science Communications 27, 201–207 (2005)Google Scholar
  7. 7.
    He, X., Jia, W.: Hexagonal Structure for Intelligent Vision. In: ICICT 2005. Proceedings of International Conference on Information and Communication Technologies, pp. 52–64 (2005)Google Scholar
  8. 8.
    He, X., Hintz, T., Wu, Q., Wang, H., Jia, W.: A New Simulation of Spiral Architecture. In: IPCV 2006. International Conference on Image Processing, Computer Vision and Pattern Recognition, pp. 570–575 (2006)Google Scholar
  9. 9.
    Tian, Y., Liu, B., Li, T.: A Local Image Interpolation Method Based on Gradient Analysis. In: ICNN&B 2005. International Conference on Neural Networks and Brain, vol. 2, pp. 1202–1205 (2005)Google Scholar
  10. 10.
    Sheridan, P., Hintz, T., Alexander, D.: Pseudo-invariant Image Transformations on a Hexagonal Lattice. Image and Vision Computing 18, 907–917 (2000)CrossRefGoogle Scholar
  11. 11.
    He, X., Wang, H., Hur, N., Jia, W., Wu, Q., Kim, J., Hintz, T.: Uniformly Partitioning Images on Virtual Hexagonal Structure. In: IEEE ICARCV 2006. 9th International Conference on Control, Automation, Robotics and Vision, pp. 891–896. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiangjian He
    • 1
  • Jianmin Li
    • 2
  • Tom Hintz
    • 1
  1. 1.Computer Vision Research Group, University of Technology, SydneyAustralia
  2. 2.School of Computer and Mathematics, Fuzhou University, Fujian, 320002China

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