Shape recognition using eigenvalues of the Dirichlet Laplacian

Authors:
M. A. Khabou;L. Hermi;M. B. H. Rhouma
Affiliations:
Electrical & Computer Engineering Department, University of West Florida, 11000 University Parkway Pensacola, FL 32514, USA;Department of Mathematics, University of Arizona, Tucson, AZ 85721-0089, USA;Department of Mathematics and Statistics, Sultan Qaboos University, Alkhod 123, Muscat, Oman
Venue:
Pattern Recognition
Year:
2007

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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.