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Spatial autoregressive moving average (ARMA) processes have been extensively used in several applications in image/signal processing. In particular, these models have been used for image segmentation, edge detection and image filtering. Image restoration algorithms based on robust estimation of a two-dimensional process have been developed (Kashyap & Eom 1988). Also the two-dimensional autoregressive model has been used to perform unsu‐ pervised texture segmentation (Cariou & Chehdi, 2008). Generalizations of the previous al‐ gorithms using the generalized M estimators to deal with the effect caused by additive contamination was also addressed (Allende et al., 2001). Later on, robust autocovariance (RA) estimators for two dimensional autoregresive (AR-2D) processes were introduced (Oje‐ da, 2002). Several theoretical contributions have been suggested in the literature, including the asymptotic properties of a nearly unstable sequence of stationary spatial autoregressive processes (Baran et al., 2004). Other contributions and applications of spatial ARMA proc‐ esses have been considered in many publications (Basu & Reinsel, 1993, Bustos 2009a, Fran‐ cos & Friendlaner1998, Guyon 1982, Ho 2011, Illig & Truong-Van 2006, Martin1996, Vallejos & Mardesic 2004).

Image Segmentation and Time Series Clustering Based on Spatial and Temporal ARMA Processes