Shape recognition using eigenvalues of the Dirichlet Laplacian

M. A. Khabou, L. Hermi, M. B.H. Rhouma

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

The eigenvalues of the Dirichlet 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. The features are also shown to be tolerant of noise and boundary deformation. These features are used to classify hand-drawn, synthetic, and natural shapes with correct classification rates ranging from 88.9% to 99.2%. The classification was done using few features (only two features in some cases) and simple feedforward neural networks or minimum Euclidian distance.

Original languageEnglish (US)
Pages (from-to)141-153
Number of pages13
JournalPattern Recognition
Volume40
Issue number1
DOIs
StatePublished - Jan 1 2007

Keywords

  • Dirichlet boundary condition
  • Eigenvalues
  • Fixed membrane problem
  • Laplacian
  • Neural networks
  • Shape recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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