Laplacian and bilaplacian based features for shape classification

Mohamed B.H. Rhouma, Lotfi Hermi, Mohamed A. Khabou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Finite difference schemes of various eigenvalue problems are used to generate size, rotation, and translation invariant sets of features for shape recognition and classification of binary images. These feature sets are based on the eigenvalues of the Dirichlet Laplacian, the clamped plate problem, and the buckling problem. The stability and effectiveness of these features is demonstrated by using them in the classification of 6 types of computer generated and hand-drawn shapes. The classification was done using 4 to 20 features fed to simple feed-forward neural networks trained using the backpropagation algorithm. All features performed very well and correct classification rates of up to 99.7% were achieved on the computer generated shapes and 97.2% on the hand-drawn shapes.

Original languageEnglish (US)
Title of host publicationProceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Pages615-619
Number of pages5
StatePublished - Dec 1 2009
Event2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 - Las Vegas, NV, United States
Duration: Jul 13 2009Jul 16 2009

Publication series

NameProceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
Volume2

Other

Other2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
CountryUnited States
CityLas Vegas, NV
Period7/13/097/16/09

Keywords

  • Buckling problem
  • Clamped plate
  • Dirichlet Laplacian

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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  • Cite this

    Rhouma, M. B. H., Hermi, L., & Khabou, M. A. (2009). Laplacian and bilaplacian based features for shape classification. In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 (pp. 615-619). (Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009; Vol. 2).