1,671 research outputs found
Morphological filtering on hypergraphs
The focus of this article is to develop computationally efficient
mathematical morphology operators on hypergraphs. To this aim we consider
lattice structures on hypergraphs on which we build morphological operators. We
develop a pair of dual adjunctions between the vertex set and the hyper edge
set of a hypergraph H, by defining a vertex-hyperedge correspondence. This
allows us to recover the classical notion of a dilation/erosion of a subset of
vertices and to extend it to subhypergraphs of H. Afterward, we propose several
new openings, closings, granulometries and alternate sequential filters acting
(i) on the subsets of the vertex and hyperedge set of H and (ii) on the
subhypergraphs of a hypergraph
Class specific feature selection for identity validation using dynamic signatures
Classification of the biometrics data for identity validation can be modeled as a single-class problem, where the identity is confirmed by comparing the biometrics of the unknown person with those of the claimed identity. However, current feature selection techniques do not differentiate between single-class and multi-class problems when determining the suitable feature set and select the feature-set that is suitable for representing or discriminating for all the available classes. This may not be the best representation of the biometrics data of an individual because different people may have differences in the most suitable features to represent their biometrical data. In this paper, a class-specific feature selection method has been proposed and experimentally validated using dynamic signatures. This method is based on the coefficient of variance within the feature set, where the features with smaller variance are selected and the ones with larger variance are rejected. The proposed technique was compared with the other feature selection methods, and the results show that a significant improvement in the classification accuracy, specificity and sensitivity was obtained when using class-specific feature selection
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