Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

Abstract : This paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N random markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradient-like functions are then segmented by a volume-based watershed algorithm to define the R largest regions. The various gradients are computed in multispectral image space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images.
Type de document :
Communication dans un congrès
Bruno Apolloni et al. Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, Sep 2007, Vietri sul Mare, Salerno, Italy. Springer Berlin Heidelberg, Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III, LNAI 4694 (Part III), pp.17-24, 2007, 〈http://dx.doi.org/10.1007/978-3-540-74829-8_3〉. 〈10.1007/978-3-540-74829-8_3〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01263963
Contributeur : Guillaume Noyel <>
Soumis le : vendredi 29 janvier 2016 - 18:30:07
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02

Licence


Copyright (Tous droits réservés)

Identifiants

Collections

Citation

Guillaume Noyel, Jesus Angulo, Dominique Jeulin. Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation. Bruno Apolloni et al. Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, Sep 2007, Vietri sul Mare, Salerno, Italy. Springer Berlin Heidelberg, Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III, LNAI 4694 (Part III), pp.17-24, 2007, 〈http://dx.doi.org/10.1007/978-3-540-74829-8_3〉. 〈10.1007/978-3-540-74829-8_3〉. 〈hal-01263963〉

Partager

Métriques

Consultations de la notice

210

Téléchargements de fichiers

90