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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





Autor: Guillaume Noyel - Jesus Angulo - Dominique Jeulin -

Fuente: https://hal.archives-ouvertes.fr/



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