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Application of Zernike Moments to the problem of General Shape Analysis

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Języki publikacji
EN
Abstrakty
EN
The Zernike Moments (ZM) have been successfully applied to the problem of shape recognition. Their properties allow for solving some fundamental problems in this task. Amongst them the most important one is the invariance to rotation, scaling, translation, and reflectional symmetry. Moreover, the obtained representation can vary according to the level of generalisation of a shape. For this reason in the paper the application of the ZM to the problem of General Shape Analysis (GSA) is proposed and experimentally investigated. The GSA problem is similar to the recognition and retrieval of shapes. However, only the most general classes of shapes (e.g. square, triangle, circle, ellipse) are assumed to perform the role of the basic templates. Moreover, the processed object does not have to belong to any of the template classes, but may be only similar to one of them. This enables us to receive the most general information about a shape, e.g. how square, triangular, round, elliptical, etc. it is. In the paper, in order to evaluate the Zernike Moments applied to the problem of GSA, the performance of this shape descriptor is compared with the results provided by nearly two hundred humans and collected by means of appropriate inquiry forms.
Rocznik
Strony
515--526
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
  • West Pomeranian University of Technology, Szczecin Faculty of Computer Science and Information Technology Żołnierska 52, 71-210 Szczecin, Poland
Bibliografia
  • Bharathi, V.S. and Ganesan L. (2008) Orthogonal moments based texture analysis of CT liver images. Pattern Recognition Letters, 29 (13), 1868- 1872.
  • Broumandnia, A. and Shanbehzadeh, J. (2007) Fast Zernike wavelet moments for Farsi character recognition. Image and Vision Computing, 25 (5), 717-726.
  • Chang, K. (2000) LANS Repository. http://www.lansece.utexas.edu/˜lans/.
  • Cheng-Song, C., San-Ben, C., Jin-Quan, L. and Ying-Dong, Q. (2005) A fast subpixel edge detection method using Sobel-Zernike moments operator. Image and Vision Computing, 23 (1), 11-17.
  • Frejlichowski, D. (2010a) An Experimental Comparison of Seven Shape Descriptors in the General Shape Analysis Problem. Lecture Notes in Computer Science, 6111, 294-305.
  • Frejlichowski, D. (2010b) An Experimental Comparison of Moment Shape Descriptors in the General Shape Analysis. Informatyka ku Przyszłości, 351-360.
  • Frejlichowski, D. and Forczmański P. (2010) General Shape Analysis Applied to Stamps Retrieval from Scanned Documents. Lecture Notes in Artificial Intelligence, 6304, 251-260.
  • Haddadnia, J., Faez, K. and Moallem, P. (2001) Neural network based face recognition with moment invariants. Proc. of 2001 International Conference on Image Processing, 1, 1018-1021.
  • Hse, H. and Newton, A.R. (2004) Sketched Symbol Recognition using Zernike Moments. Proc. of the 17th International Conference on Pattern Recognition (ICPR’04), 1, 367-370.
  • Ji, Z., Chen, Q., Sun, Q.-S. and Xia, D.-S. (2009) A moment-based nonlocal- means algorithm for image denoising. Information Processing Letters, 109, 23-24, 1238-1244.
  • Pang,Y.H., Andrew,T.B.J., David,N.C.L. and Hiew,F.S. (2004) Palmprint verification with moments. Journal of Computer Graphics, Visualization and Computer Vision, 12 (2), 325-332.
  • Shutler, J.D. and Nixon, M.S. (2008) Zernike velocity moments for sequence-based description of moving features. Image and Vision Computing, 24 (4), 343-356.
  • Teague, M.R. (1980) Image analysis via the general theory of moments. Journal of Optical Society of America, 70, 920-930.
  • Wee, C.-Y. and Paramesran, R. (2007) On the computational aspects of Zernike moments. Image and Vision Computing, 25 (6), 967-980.
  • Xin, Y., Liao, S. and Pawlak, M. (2004) Geometrically robust image watermarking via pseudo-Zernike moments. Proc. of 2004 Canadian Conference on Electrical and Computer Engineering, 2, 939-942.
  • Yadav, R., Bahadur, N., Naveen, K., Gupta, A.K., Rastogi, V.K. (2008) Retrieval and classification of objects using generic Fourier, Legendre moment, and wavelet Zernike moment descriptors and recognition using joint transform correlator. Optics and Laser Technology, 40 (3), 517-527.
  • Zhang, F., Liu, S.-Q., Wang, D.-B. and Guan, W. (2009) Aircraft recognition in infrared image using wavelet moment invariants. Image and Vision Computing, 27 (4), 313-318.
  • Zhenjiang, M. (2009) Zernike moment-based image shape analysis and its application. Pattern Recognition Letters, 21 (2), 169-177.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATC-0008-0014
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