Application of the Moment Shape Representations to the General Shape Analysis

Dariusz Frejlichowski


The General Shape Analysis (GSA) is a task similar to the shape recognition and retrieval. However, in GSA an object usually does not belong to a template class, but can only be
similar to some of them. Moreover, the number of applied templates is limited. Usually,
ten most general shapes are used. Hence, the GSA consists in searching for the most universal information about them. This is useful when some general information has to be concluded, e.g. in coarse classification. In this paper the result of the application of three shape descriptors based on the moment theory to the GSA is presented. For this purpose the Moment Invariants, Contour Sequence Moments, and Zernike Moments were selected.


Bator, M., Chmielewski, L.J. (2009). Finding regions of interest for cancerous masses enhanced by elimination of linear structures and considerations on detection correctness measures in mammography. Pattern Analysis and Applications, 12(4), 377–390

Hu, M.K. (1962). Visual Pattern Recognition by Moment Invariants. IEEE Transactions on Information Theory, 8, 179–187

Hupkens, Th.M., Clippeleir, J. de (1995). Noise and Intensity Invariant Moments. Pattern Recognition Letters, 16 (4), 371–376

Khan, M.S., Coenen, F., Dixon, C., El-Salhi, S. (2012). A Classification Based Approach for Predicting Springback in Sheet Metal Forming. Journal of Theoretical and Applied Computer Science 6(2), 45–59

Forczmanski P., Frejlichowski D. (2010). Robust Stamps Detection and Classification by Means of General Shape Analysis. Lecture Notes in Computer Science, 6374, 360–367

Frejlichowski, D. (2010). An Experimental Comparison of Seven Shape Descriptors in the General Shape Analysis Problem. Lecture Notes in Computer Science, 6111, 294–305

Frejlichowski, D. (2011). The Application of the Zernike Moments to the Problem of General Shape Analysis. Control and Cybernetics, 40(2), 515–526

Frejlichowski, D., Forczmanski, P. (2010). General Shape Analysis Applied to Stamps Retrieval from Scanned Documents. Lecture Notes in Computer Science, 6304, 251–260

Liu, C.-B., Ahuja, N. (2004). Vision Based Fire Detection. Proc. of the 17th Int. Conf. on Pattern Recognition, ICPR 2004, Cambridge, UK

Oszutowska-Mazurek, D., Mazurek, P., Sycz, K., Waker-Wojciuk, G. (2012). Estimation of Fractal Dimension According to Optical Density of Cell Nuclei in Papanicolaou Smears. Lecture Notes in Computer Science, 7339, 456–463

Reverter, F., Rosado, P., Figueras, E., Planas, M.A. (2012). Computer vision methods for image-based artistic ideation. Journal of Theoretical and Applied Computer Science, 6(2), 72–78

Rosin, P.L. (1999). Measuring Rectangularity. Machine Vision and Applications, 11, 191–196

Rosin, P.L. (2003). Measuring Shape: Ellipticity, Rectangularity and Triangularity. Machine Vision and Applications, 14, 172–184

Rosin, P.L. (2005). Computing Global Shape Measures. In: Chen, C.H., Wang, P.S.P. (Eds.) Handbook of Pattern Recognition and Computer Vision, 3rd edn., 177–196

Rothe, I., Susse, H., Voss, K. (1996). The Method of Normalization to Determine Invariants. IEEE Trans. On Pattern Analysis and Machine Intelligence, 18, 366–375

Sonka, M., Hlavac, V., Boyle, R. (1998). Image Processing, Analysis, and Machine Vision The book (2nd Edition)

Wee, C.-Y., Paramesran, R. (2007). On the Computational Aspects of Zernike Moments. Image and Vision Computing, 25 (6), 967–980


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