TY - GEN
T1 - Unsupervised segmentation of non stationary data hidden with non stationary noise
AU - El Yazid Boudaren, Mohamed
AU - Pieczynski, Wojciech
AU - Monfrini, Emmanuel
PY - 2011/8/3
Y1 - 2011/8/3
N2 - Classical hidden Markov chains (HMC) can be inefficient in the unsupervised segmentation of non stationary data. To overcome such involvedness, the more elaborated triplet Markov chains (TMC) resort to using an auxiliary underlying process to model the behavior switches within the hidden states process. However, so far, only this latter was considered non stationary. The aim of this paper is to extend the results of a recently proposed TMC by considering both hidden states and noise non stationary. To show the efficiency of the proposed model, we provide results of non stationary synthetic and real images restoration.
AB - Classical hidden Markov chains (HMC) can be inefficient in the unsupervised segmentation of non stationary data. To overcome such involvedness, the more elaborated triplet Markov chains (TMC) resort to using an auxiliary underlying process to model the behavior switches within the hidden states process. However, so far, only this latter was considered non stationary. The aim of this paper is to extend the results of a recently proposed TMC by considering both hidden states and noise non stationary. To show the efficiency of the proposed model, we provide results of non stationary synthetic and real images restoration.
U2 - 10.1109/WOSSPA.2011.5931466
DO - 10.1109/WOSSPA.2011.5931466
M3 - Conference contribution
AN - SCOPUS:79960896452
SN - 9781457706905
T3 - 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011
SP - 255
EP - 258
BT - 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011
T2 - 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011
Y2 - 9 May 2011 through 11 May 2011
ER -