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Conference papers

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.
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Contributor : Guillaume Noyel Connect in order to contact the contributor
Submitted on : Friday, January 29, 2016 - 6:30:07 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:14 PM





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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