Performance comparison of laplacian-based features

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

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

3 Citations (Scopus)

Abstract

Three feature sets based on the eigenvalues of the Laplacian operator with different boundary conditions were generated and their computational complexity and effectiveness in a pattern recognition application were compared. The first feature set is based on the eigenvalues of the Laplacian operator with Dirichlet boundary condition, the second feature set uses Neumann boundary condition, and the third set uses Stekloff boundary condition. All feature sets are rotation, translation, and size invariant. The effectiveness of these features is demonstrated by using them in the classification of 5 types of binary hand-drawn shapes. The classification was done using 4 to 20 features fed to a simple feed-forward neural network. Even though the Dirichlet and Neumann feature sets were computationally more complex than the Stekloff features, they were more tolerant of input variations and clearly outperformed the Stekloff feature sets in the pattern classification application. The correct classification rates of the Stekloff feature sets ranged from 34.0% to 61.0% while those of the Dirichlet and Neumann feature sets were 60.0%-95.5% and 87.5-95.5%, respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
Pages39-43
Number of pages5
StatePublished - 2008
Event2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008 - Las Vegas, NV, United States
Duration: Jul 14 2008Jul 17 2008

Other

Other2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
CountryUnited States
CityLas Vegas, NV
Period7/14/087/17/08

Fingerprint

Boundary conditions
Pattern recognition
Mathematical operators
Feedforward neural networks
Computational complexity

Keywords

  • Dirichlet
  • Laplacian egenvalues
  • Neumann
  • Stekloff

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Khabou, M. A., Rhouma, M. B. H., & Hermi, L. (2008). Performance comparison of laplacian-based features. In Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008 (pp. 39-43)

Performance comparison of laplacian-based features. / Khabou, Mohamed A.; Rhouma, Mohamed B H; Hermi, Lotfi.

Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008. 2008. p. 39-43.

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

Khabou, MA, Rhouma, MBH & Hermi, L 2008, Performance comparison of laplacian-based features. in Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008. pp. 39-43, 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008, Las Vegas, NV, United States, 7/14/08.
Khabou MA, Rhouma MBH, Hermi L. Performance comparison of laplacian-based features. In Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008. 2008. p. 39-43
Khabou, Mohamed A. ; Rhouma, Mohamed B H ; Hermi, Lotfi. / Performance comparison of laplacian-based features. Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008. 2008. pp. 39-43
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