The use of a client-side buffer in the delivery of compressed prerecorded video can be an effective tool for removing the burstiness required of the underlying server and network by smoothing the bandwidth requirements for continuous delivery. Given a fixed-size smoothing buffer, several bandwidth smoothing algorithms have been introduced in the literature that are provably optimal under certai...

Channel smoothing is an alternative to diffusion filtering for robust estimation of image features. Its main advantages are speed, stability with respect to parameter changes, and a simple implementation. However, channel smoothing becomes instable in certain situations, typically for elongated, periodic patterns like for instance fingerprints. As for the diffusion filtering an anisotropic exte...

Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strong...

This work presents a new approach for analyzing the problem of edge-preserving image smoothing using convex minimization and for selecting smoothing parameters. The close-form (global) solution is derived as the response of a convex smoothing model to the ideal step edge. Insights into how the minimal solution responds to edges in the data and how the parameter values aaect resultant edges in t...

The prior model or penalizing term in Bayesian image analysis is typically a Markov random eld parametrized by one or more smoothing parameters. For many commonly applied Markov random eld penalizing terms there do not exist both objective and practically applicable methods for choosing the smoothing parameters. In this paper we discuss approaches to analysis of residuals in a simple case of im...

We study a class of linear programming (LP) problems motivated by large-scale machine learning applications. After reformulating the LP as a convex nonsmooth problem, we apply Nesterov’s primal-dual smoothing technique. It turns out that the iteration complexity of the smoothing technique depends on a parameter θ that arises because we need to bound the originally unbounded primal feasible set....

We propose a locally regularized snake based on smoothing-spline filtering. The proposed algorithm associates a regularization process with a force equilibrium scheme leading the snake’s deformation. In this algorithm, the regularization is implemented with a smoothing of the deformation forces. The regularization level is controlled through a unique parameter that can vary along the contour. I...

We generalize a smoothing algorithm for finite min–max to finite min– max–min problems. We apply a smoothing technique twice, once to eliminate the inner min operator and once to eliminate the max operator. In mini–max problems, where only the max operator is eliminated, the approximation function is decreasing with respect to the smoothing parameter. Such a property is convenient to establish ...

Untuk mendapatkan nilai alpha paling optimal untuk metode Brown's Double Exponential Smoothing (Brown's-DES) dapat menggunakan algoritma genetika. Namun, Kekurangan dari genetika yaitu memerlukan generasi yang banyak menghasilkan sebuah optimal. Perlu iterasi harus dilakukan jika ingin dan presisi. Banyaknya akan berpengaruh pada kecepatan dalam menemukan Maka itu, peneliti mengembangkan multip...

An important element of both exploratory data analysis and many dimensionality reduction techniques is a scatterplot smoother. In both areas there is a strong need for fast and simple procedures. A major hurdle is choice of the amount of smoothing. In this paper we propose a fast and simple choice of the scatterplot smoothing parameter, based on blockwise least squares parabolic tting. Our meth...