Mohamed A. Khabou

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tn. rnu. fsegs @ maghrebi. wafa , fr. yahoo @ anisborchani , org. ieee @ alimi. adel Abstract We present a novel image indexing and retrieval system based on object contour description. Extended curvature scale space (CSS) descriptors composed of both local and global features are used to represent and index concave and convex object shapes. These features(More)
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,(More)
Standard shared-weight neural networks previously demonstrated inferior performance to that of morphological shared-weight neural networks for automatic target detection. Empirical analysis showed that entropy measures of the features generated by the standard shared-weight neural networks were consistently lower than those generated by the morphological(More)
In this paper, we introduce a new variant of growing self-organizing maps (GSOM) based on Alahakoon's algorithm for SOM training; so called 2IBGSOM (interior and irregular boundaries growing self-organizing maps). It's dynamically evolving structure for SOM, which allocates map size and shape during the unsupervised training process. 2IBGSOM starts with a(More)