TY - GEN
T1 - Estimation of mixture and unsupervised segmentation of images
AU - Marhic, Nicole
AU - Pieczynski, Wojciech
PY - 1991/12/1
Y1 - 1991/12/1
N2 - A statistical approach is presented to the Bayesian unsupervised segmentation of images. The main problem lies in the estimation of a distribution mixture. Iterative conditional estimation provides solutions to such a problem. After a brief review of the general procedure, two stochastic algorithms are described in the case of a finite Gaussian mixture. They are applied to synthetic images corrupted by Gaussian noise in order to estimate the parameters required when performing contextual Bayesian segmentation.
AB - A statistical approach is presented to the Bayesian unsupervised segmentation of images. The main problem lies in the estimation of a distribution mixture. Iterative conditional estimation provides solutions to such a problem. After a brief review of the general procedure, two stochastic algorithms are described in the case of a finite Gaussian mixture. They are applied to synthetic images corrupted by Gaussian noise in order to estimate the parameters required when performing contextual Bayesian segmentation.
UR - https://www.scopus.com/pages/publications/0026373859
M3 - Conference contribution
AN - SCOPUS:0026373859
SN - 0879426756
T3 - Digest - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1083
EP - 1086
BT - Digest - International Geoscience and Remote Sensing Symposium (IGARSS)
A2 - Anon, null
PB - Publ by IEEE
T2 - 1991 International Geoscience and Remote Sensing Symposium - IGARSS'91
Y2 - 3 June 1991 through 6 June 1991
ER -