An Experimental Comparison of Fourier-Based Shape Descriptors in the General Shape Analysis Problem

  • Katarzyna GościewskaEmail author
  • Dariusz Frejlichowski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)


The General Shape Analysis (GSA) is a problem similar to the typical recognition or retrieval of shapes, but it does not aim at the exact shape identification. The main goal is to find one or few most similar general templates, such as rectangle or triangle, for an investigated object. That allows for obtaining the most basic information about a shape. In this paper the experimental results on the application of three Fourier-based shape descriptors to the GSA problem are provided. The Euclidean distance is applied for measuring the dissimilarity between represented objects. In order to estimate the effectiveness of investigated shape descriptors the results of the experiments were compared with human benchmark results, collected by means of appropriate inquiry forms.


Test Object Polar Coordinate System Shape Representation Fourier Descriptor Region Shape 
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.
    Brechner, S., Ade, F.: An Automatic System for the Classification of Microfossils. In: Proceedings of the 11th Scandinavian Conference on Image Analysis, vol. 2, pp. 825–832 (1999)Google Scholar
  2. 2.
    Dryden, I.L.: General shape and registration analysis. Stochastic Geometry: Likelihood and Computation (1997)Google Scholar
  3. 3.
    Forczmański, P., Frejlichowski, D.: Robust Stamps Detection and Classification by Means of General Shape Analysis. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 360–367. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Frejlichowski, D.: An Algorithm for Binary Contour Objects Representation and Recognition. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 537–546. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Frejlichowski, D.: An Experimental Comparison of Seven Shape Descriptors in the General Shape Analysis Problem. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6111, pp. 294–305. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Frejlichowski, D.: Pre-processing, Extraction and Recognition of Binary Erythrocyte Shapes for Computer-Assisted Diagnosis Based on MGG Images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 368–375. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Frejlichowski, D.: Analysis of four polar shape descriptors properties in an exemplary application. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 376–383. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Frejlichowski, D.: Analysis of possible system-level hardware implementation of selected shape description algorithms. Journal of Theoretical and Applied Computer Science 6(4), 51–58 (2012)Google Scholar
  9. 9.
    Gleissberg, S.: Comparative Analysis Of Leaf Shape Development In Eschscholzia Californica And Other Papaveraceae-Schscholzioideae. Am J. Bot. 91(3), 306–312 (2004)CrossRefGoogle Scholar
  10. 10.
    Gut, P., Chmielewski, L., Kukołowicz, P., Dąbrowski, A.: Edge-based robust image registration for incomplete and partly erroneous data. In: Skarbek, W. (ed.) CAIP 2001. LNCS, vol. 2124, pp. 309–316. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Krupiński, R., Mazurek, P.: Electrooculography Signal Estimation by Using Evolution–Based Technique for Computer Animation Applications. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 139–146. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kukharev, G.: Digital Image Processing and Analysis. SUT Press, Szczecin (1998) (in Polish)Google Scholar
  13. 13.
    Lindsay, R., Smith, G., Atchison, D.: Descriptors of corneal shape. Optometry Vision Sci. 75(2), 156–158 (1998)CrossRefGoogle Scholar
  14. 14.
    Rauber, T.W.: Two Dimensional Shape Description. Technical report: GR UNINOVA-RT-10-94. Universidade Nova de Lisboa, Lisoba, Portugal (1994)Google Scholar
  15. 15.
    Richards, R.A., Esteves, C.: Use of scale morphology for discriminating wild stocks of Atlantic striped bass. T. Am Fish Soc. 126(6), 919–925 (1997)CrossRefGoogle Scholar
  16. 16.
    Rosin, P.L.: Measuring Rectangularity. Mach. Vision Appl. 11(4), 191–196 (1999)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rosin, P.L.: Measuring Shape: Ellipticity, Rectangularity, and Triangularity. Mach. Vision Appl. 14(3), 172–184 (2003)Google Scholar
  18. 18.
    Rosin, P.L.: Computing Global Shape Measures. In: Chen, C.H., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, 3rd edn., pp. 177–196 (2005)Google Scholar
  19. 19.
    Rosin, P.L., Ẑunić J: Measuring Squareness and Orientation of Shapes. J. Math. Imaging Vis. 39, 13–27 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Zhang, D., Lu, G.: Shape-Based Image Retrieval Using Generic Fourier Descriptor. Signal Process-Image 17(10), 825–848 (2002)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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