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2007
Springer

Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

9 years 10 months ago
Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation
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.
Guillaume Noyel, Jesús Angulo, Dominique Je
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where KES
Authors Guillaume Noyel, Jesús Angulo, Dominique Jeulin
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