TY - GEN
T1 - Unsupervised segmentation of non stationary images with non Gaussian correlated noise using triplet Markov fields and the pearson system
AU - Benboudjema, Dalila
AU - Pieczynski, Wojciech
PY - 2006/1/1
Y1 - 2006/1/1
N2 - The hidden Markov field (HMF) model has been used in many model-based solutions for image segmentation, and generally gives satisfying results. However, when the class image is non stationary, the unsupervised segmentation results provided by HMF can be poor. In this paper, we propose a new model based on triplet Markov fields (TMF) and the Pearson system which enables one to deal with non stationary hidden fields and correlated, possibly non Gaussian noise. Moreover, the nature of marginal distributions of the noise can vary with the class. We specify a new general parameter estimation method and apply it to unsupervised Bayesian image segmentation.
AB - The hidden Markov field (HMF) model has been used in many model-based solutions for image segmentation, and generally gives satisfying results. However, when the class image is non stationary, the unsupervised segmentation results provided by HMF can be poor. In this paper, we propose a new model based on triplet Markov fields (TMF) and the Pearson system which enables one to deal with non stationary hidden fields and correlated, possibly non Gaussian noise. Moreover, the nature of marginal distributions of the noise can vary with the class. We specify a new general parameter estimation method and apply it to unsupervised Bayesian image segmentation.
UR - https://www.scopus.com/pages/publications/33947654851
U2 - 10.1109/icassp.2006.1660439
DO - 10.1109/icassp.2006.1660439
M3 - Conference contribution
AN - SCOPUS:33947654851
SN - 142440469X
SN - 9781424404698
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II701-II704
BT - Image and Multidimensional Signal Processing Signal Processing Education Bio Imaging and Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Y2 - 14 May 2006 through 19 May 2006
ER -