The 2D indexed random coefficients autoregressive (2D-RCA) models are obtained by introducing appropriate random field coefficients to an AR model on Z(2). The study of such models is motivated by their capability to capture the space-varying behavior of the volatility. A generalized method of moment approach is considered to estimate the 2D-RCA models. Consistency and asymptotic normality of the estimates are derived. Estimated parameters are used, at a later stage, as pixel features in texture image classification.
In this paper a new subspaces clustering algorithm is proposed. This method has two levels, the first one is an iterative algorithm based on the minimization of an objective function. The density is introduced in this objective function where the distances between points become relatively uniform in high dimensional spaces. In such cases, the density of cluster may give better results. The idea of the second level is to find the clusters in each subspace individually. We applied the proposed method to medical tomography scan image without Intravenous or IV contrast dye. Then we compare the results with the same image with IV contrast. However in some cases, there are risks associated with this injection, where the mortality risk is low but not null. This method can reduce the use of this injection. Experimental results on synthetic and real datasets show that the proposed method gives good results in medical tomography image.
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