Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

Guillaume Noyel 1 Jesus Angulo 1 Dominique Jeulin 1
1 CMM - Centre de Morphologie Mathématique
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.
Keywords : unsupervised multispectral image mathematical morphology stochastic watershed hyperspectral image probabilistic watershed multivariate image segmentation
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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>
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Contributeur : Guillaume Noyel <>
Soumis le : vendredi 29 janvier 2016 - 18:30:07
Dernière modification le : mardi 12 septembre 2017 - 11:41:18

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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>

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