Accéder directement au contenu Accéder directement à la navigation

Mine Classification based on raw sonar data: an approach combining Fourier Descriptors, Statistical Models and Genetic Algorithms

Abstract : In the context of mine warfare, detected mines can be classified from their cast shadow. A standard solution is to perform image segmentation first (we obtain binary from graylevel image giving the label zero for pixels belonging to the shadow and the label one elsewhere), and then to perform a classification based on features extracted from the 2D-shape of the segmented shadow. Consequently, if a mistake happens during the process, it will be propagated through the following steps. In this paper, to avoid such drawbacks, we propose a novel approach where a dynamic segmentation scheme is fully classification-oriented. Actually, classification is performed directly from the raw image data. The approach is based on the combination of deformable models, genetic algorithms, and statistical image models.
Liste complète des métadonnées

Littérature citée [9 références]  Voir  Masquer  Télécharger

https://hal-ensta-bretagne.archives-ouvertes.fr/hal-00504817
Contributeur : Isabelle Quidu <>
Soumis le : mercredi 21 juillet 2010 - 15:15:05
Dernière modification le : jeudi 19 décembre 2019 - 01:28:31
Document(s) archivé(s) le : vendredi 22 octobre 2010 - 16:13:23

Fichier

Oceans00geneticAlgos.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00504817, version 1

Citation

Isabelle Quidu, Jean-Philippe Malkasse, Gilles Burel, Pierre Vilbé. Mine Classification based on raw sonar data: an approach combining Fourier Descriptors, Statistical Models and Genetic Algorithms. IEEE OCEANS'2000, Sep 2000, Providence, Rhode Island, United States. ⟨hal-00504817⟩

Partager

Métriques

Consultations de la notice

614

Téléchargements de fichiers

407