Knowledge from markers in watershed segmentation

Abstract : Due to its broad impact in many image analysis applications, the prob- lem of image segmentation has been widely studied. However, there still does not exist any automatic segmentation procedure able to deal accurately with any kind of image. Thus semi-automatic segmentation methods may be seen as an appropriate alternative to solve the segmentation problem. Among these methods, the marker-based watershed has been successfully involved in various domains. In this algorithm, the user may locate the markers, which are used only as the initial starting positions of the regions to be segmented. We propose to base the segmentation process also on the contents of the markers through a supervised pixel classification, thus resulting in a knowledge-based watershed segmentation where the knowledge is built from the markers. Our contribution has been evaluated through some comparative tests with some state-of-the-art methods on the well-known Berkeley Segmentation Dataset.
Type de document :
Communication dans un congrès
12th International Conference on Computer Analysis of Images and Patterns (CAIP 2007), 2007, Austria. Springer, 4673, pp.579-586, 2007, Lecture Notes in Computer Science. <10.1007/978-3-540-74272-2_72>
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00515912
Contributeur : Sébastien Lefèvre <>
Soumis le : mercredi 8 septembre 2010 - 11:37:28
Dernière modification le : jeudi 9 septembre 2010 - 11:58:49
Document(s) archivé(s) le : mardi 23 octobre 2012 - 15:45:18

Fichier

caip2007.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Sébastien Lefèvre. Knowledge from markers in watershed segmentation. 12th International Conference on Computer Analysis of Images and Patterns (CAIP 2007), 2007, Austria. Springer, 4673, pp.579-586, 2007, Lecture Notes in Computer Science. <10.1007/978-3-540-74272-2_72>. <hal-00515912>

Partager

Métriques

Consultations de
la notice

73

Téléchargements du document

73