TY - GEN
T1 - Fuzzy statistical unsupervised image segmentation
AU - Caillol, H.
AU - Pieczynski, W.
N1 - Publisher Copyright:
© IEEE 1992.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - This paper deals with fuzzy Bayesian unsupervised image segmentation. At first, we introduce a new model and a method for its simulation. The images obtained that way are corrupted with Gaussian, white or correlated, noise. A blind Bayesian segmentation is performed using parameters estimated by the SEM algorithm adapted to our model. Finally this segmentation is compared with a classical method without taking into account the fuzzy class.
AB - This paper deals with fuzzy Bayesian unsupervised image segmentation. At first, we introduce a new model and a method for its simulation. The images obtained that way are corrupted with Gaussian, white or correlated, noise. A blind Bayesian segmentation is performed using parameters estimated by the SEM algorithm adapted to our model. Finally this segmentation is compared with a classical method without taking into account the fuzzy class.
UR - https://www.scopus.com/pages/publications/84964461169
U2 - 10.1109/IGARSS.1992.578461
DO - 10.1109/IGARSS.1992.578461
M3 - Conference contribution
AN - SCOPUS:84964461169
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1391
EP - 1394
BT - IGARSS 1992 - International Geoscience and Remote Sensing Symposium
A2 - Williamson, Ruby
A2 - Stein, Tammy
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992
Y2 - 26 May 1992 through 29 May 1992
ER -