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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.
https://hal.archives-ouvertes.fr/hal-01263963
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
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