In this paper we propose a smoothing method for non smooth signals, which control the geometry of a sampled signal. The signal is considered as a geometric object and the smoothing is done using a smoothing kernel function that controls the curvature of the obtained smooth signal in a close neighborhood of a metric curvature measure of the original signal.

Robust versions of the exponential and Holt–Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in the presence of outliers. The robust exponential and Holt–Winters smoothing methods are presented as recursive updating schemes that apply the standard technique to pre-cleaned data. Both the update equation and the selection of the smoo...

Smoothing filter is the method of choice for image preprocessing and pattern recognition. We present a new concurrent method for smoothing 2D object in binary case. Proposed method provides a parallel computation while preserving the topology by using homotopic transformations. We introduce an adapted parallelization strategy called split, distribute and merge (SDM) strategy which allows effici...

We propose a new class of non-linear image recovery operators based on local smoothing applied both in the spatial domain (horizontal smoothing) and in the gray-level domain (vertical smoothing). The commonly used horizontal smoothing gives the way of reducing the noise level (noise filtering), whereas the vertical smoothing yields the edge preservation property. The method is indexed by a real...

In this article we propose a method for solving unconstrained optimization problems with convex and Lipschitz continuous objective functions. By making use of the Moreau envelopes of the functions occurring in the objective, we smooth the latter to a convex and differentiable function with Lipschitz continuous gradient by using both variable and constant smoothing parameters. The resulting prob...

An approach to exponential smoothing that relies on a linear single source of error state space model is outlined. A maximum likelihood method for the estimation of associated smoothing parameters is developed. Commonly used restrictions on the smoothing parameters are rationalised. Issues surrounding model identi…cation and selection are also considered. It is argued that the proposed revised ...

In this paper, we present a probabilistic approach for 3D object’s smoothing. The core idea behind the proposed method is to relate the problem of smoothing objects to that of tracking the transition probability density functions of an underlying random process. We show that such an approach allows for additional insight and sufficient flexibility compared with existing standard smoothing techn...