TY - GEN
T1 - Segmenting non stationary images with triplet Markov fields
AU - Benboudjema, Dalila
AU - Pieczynski, Wojciech
PY - 2005/1/1
Y1 - 2005/1/1
N2 - The hidden Markov field (HMF) model has been used in many model-based solutions to image analysis problems, including that of 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 tackle the problem of modeling a non stationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (IMF) model, to segment non stationary images. Experiments indicate that the new algorithm performs better than the classical one.
AB - The hidden Markov field (HMF) model has been used in many model-based solutions to image analysis problems, including that of 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 tackle the problem of modeling a non stationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (IMF) model, to segment non stationary images. Experiments indicate that the new algorithm performs better than the classical one.
U2 - 10.1109/ICIP.2005.1529751
DO - 10.1109/ICIP.2005.1529751
M3 - Conference contribution
AN - SCOPUS:33749606418
SN - 0780391349
SN - 9780780391345
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 317
EP - 320
BT - IEEE International Conference on Image Processing 2005, ICIP 2005
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing 2005, ICIP 2005
Y2 - 11 September 2005 through 14 September 2005
ER -