Feature generation using the Laplacian operator with Neumann boundary condition

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

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

3 Scopus citations

Abstract

The eigenvalues of the Neumann Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation, translation, and size invariant and are shown to be tolerant of boundary deformation. The effectiveness of these features is demonstrated by using them to classify 5 types of computer generated and hand drawn shapes. The classification was done using 4 to 20 features fed to a simple feedforward neural network. Correct classification rates ranging from 94.4% to 100% were obtained on computer generated shapes and 67.5% to 95.5% on hand drawn shapes.

Original languageEnglish (US)
Title of host publication2007 IEEE SoutheastCon
Pages766-771
Number of pages6
DOIs
StatePublished - Aug 10 2007
Event2007 IEEE SoutheastCon - Richmond, VA, United States
Duration: Mar 22 2007Mar 25 2007

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
ISSN (Print)0734-7502

Other

Other2007 IEEE SoutheastCon
CountryUnited States
CityRichmond, VA
Period3/22/073/25/07

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Khabou, M. A., Rhouma, M. B. H., & Hermi, L. (2007). Feature generation using the Laplacian operator with Neumann boundary condition. In 2007 IEEE SoutheastCon (pp. 766-771). [4147535] (Conference Proceedings - IEEE SOUTHEASTCON). https://doi.org/10.1109/SECON.2007.343005