Shape recognition using eigenvalues of the Dirichlet Laplacian

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 2 features in some cases) and simple feedforward neural networks or minimum Euclidian distance. Key words: Shape recognition, eigenvalues, Laplacian, fixed membrane problem, Dirichlet boundary condition, neural networks. Preprint submitted to Elsevier Science 20 December 2005 

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Mohamed A. Khabou, Lotfi Hermi, Mohamed Ben Hadj Rhouma
• Keywords: Dirichlet Laplacian, Feedforward Neural Networks, Neural Networks, PR 2007