TY - GEN
T1 - Fast unsupervised statistical image segmentation method
AU - Emsalem, M.
AU - Caillol, H.
AU - Olivie, P.
AU - Carnat, G.
AU - Pieczynski, W.
N1 - Publisher Copyright:
© IEEE 1992.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - This work deals with the statistical unsupervised image segmentation. We propose a new fast algorithm based on hidden Markov chains. The originality of our approach is situated at two levels. First, the pixels are numbered according to a Peano curve and we show that it improves the efficiency of the classical "lie by line" numbering. Second, the parameter estimation phasis is performed by the use of a new general method of estimation in the case of hidden data, so called "iterative conditional estimation". The segmentation phasis is performed by the "maximiser of posteriors marginals", where the posterior marginal distributions are computed be the "backward-forward algorithm. The efficiency of our method is compared with the efficiency of a "classical" one, where the segmentation is performed by the ICM algorithm and the Markov random hidden fields parameters are estimated, using segmentations based on "current values" of parameters, by the estimator of Derin and Elliot.
AB - This work deals with the statistical unsupervised image segmentation. We propose a new fast algorithm based on hidden Markov chains. The originality of our approach is situated at two levels. First, the pixels are numbered according to a Peano curve and we show that it improves the efficiency of the classical "lie by line" numbering. Second, the parameter estimation phasis is performed by the use of a new general method of estimation in the case of hidden data, so called "iterative conditional estimation". The segmentation phasis is performed by the "maximiser of posteriors marginals", where the posterior marginal distributions are computed be the "backward-forward algorithm. The efficiency of our method is compared with the efficiency of a "classical" one, where the segmentation is performed by the ICM algorithm and the Markov random hidden fields parameters are estimated, using segmentations based on "current values" of parameters, by the estimator of Derin and Elliot.
UR - https://www.scopus.com/pages/publications/84964413898
U2 - 10.1109/IGARSS.1992.578462
DO - 10.1109/IGARSS.1992.578462
M3 - Conference contribution
AN - SCOPUS:84964413898
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1395
EP - 1397
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 -