Kiyoshi Nishiyama

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In some estimation or identification techniques, a forgetting factor has been used to improve the tracking performance for time-varying systems. However, the value of has been typically determined empirically, without any evidence of optimality. In our previous work, this open problem is solved using the framework of H optimization. The resultant H filter(More)
The use of particle filters to solve non-Gaussian, nonlinear estimation problems has attracted the attention of many researchers in recent years. The particle filters require a proposal distribution, and formulation of the proposal distribution is a critical design issue. Here a fast and effective method for generating the proposal distribution is derived(More)
The performances of adaptive filtering algorithms are critically controlled by specific tunable parameters. The convergence rate of the normalized least mean squares (NLMS) algorithm may be accelerated by adjusting the step size parameter. The tracking speed of the recursive least squares (RLS) algorithm may be improved by using the forgetting factor, which(More)
A novel image restoration method is proposed to resolve a problem that the traditional restoration method performs poorly when the kind of image degradation model from high- to low-resolution is unconfirmed. In this paper, the proposed method includes a conceptual frame of state space model (SSM) in order to achieve a general model for accurately estimating(More)
An extension of the traditional color-based visual tracker, i.e., the continuously adaptive mean shift tracker, is given for improving the convenience and generality of the color-based tracker. This is achieved by introducing a probability density function for pixels based on the hue histogram of object. As its merits, the direction and size of the tracked(More)