In image segmentation, finite mixture modeling has been widely used. In its simplest form, the spatial correlation among neighboring pixels is not taken into account, and its segmentation results can be largely deteriorated by noise in images. We propose a spatially correlated mixture model in which the mixing proportions of finite mixture models are governed by a set of underlying functions defined on the image space. The spatial correlation among pixels is introduced by putting a Gaussian process prior on the underlying functions. We can set the spatial correlation rather directly and flexibly by choosing the covariance function of the Gaussian process prior. The effectiveness of our model is demonstrated by experiments with synthetic and real images.

In this paper, we present new methods for learning the individual patterns of a card user's transaction amount and the region in which he or she uses the card, for a given period, and for determining whether the specified transaction is allowable in accordance with these learned user transaction patterns. Then, we classify legitimate transactions and fraudulent transactions by setting thresholds based on the learned individual patterns.

This paper presents a variant of the "Third-Order Recursion (TOR)" method for bispectral estimation of transfer-function parameters of a non-minimum-phase all-poles system. The modification is based on the segmentation of system-output data into coupled records, instead of independent records. It consists of considering the available data at the left and the right of each record as not null and taking them as the data corresponding to the preceding and succeeding record respectively. The proposed variant can also be interpreted as a "Constrained Third-Order Mean (CTOM)" method with a new segmentation in overlap records. Simulation results show that this new segmentation procedure gives more precise system parameters than the TOR and CTOM methods, to be obtained. Finally, in order to justify the use of bispectral techniques, the influence of added white and colored Gaussian noise on the parameter estimation is also considered.

This paper addresses the problem of estimating the parameters of multivariate ARMA processes by using higher-order statistics called cumulants. The main objective in this paper is to extend the idea of the q-slice algorithm in univariate ARMA processes to multivariate ARMA processes. It is shown for a multivariate ARMA process that the MA coefficient matrices can be estimated up to postmultiplication of a permutation matrix by using the third-order cumulants and of an extended permutation matrix by using the fourth-order cumulants. Simulation examples are included to demonstrate the effectiveness of the proposed method.