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Communication Dans Un Congrès Année : 2007

Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

Guillaume Noyel
Jesus Angulo
Dominique Jeulin

Résumé

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.
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Dates et versions

hal-01263963 , version 1 (29-01-2016)

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Citer

Guillaume Noyel, Jesus Angulo, Dominique Jeulin. Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation. 11th International Conference Knowledge-Based Intelligent Information and Engineering Systems (KES 2007), Sep 2007, Vietri sul Mare, Salerno, Italy. pp.17-24, ⟨10.1007/978-3-540-74829-8_3⟩. ⟨hal-01263963⟩
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