Sentence examples for mean filtering algorithm from inspiring English sources

Spatial context integration by the use of a mean filtering algorithm on explanatory variables increased the Kappa index on the extrapolated area by more than ten points.

Firstly, a dynamic part mean filtering algorithm is proposed to separate the direct current components, and then a limited impulse response filtering algorithm is deployed to filter out the high-frequency noise.

In [9], we propose a two-step filtering method in which a dynamic part mean filtering algorithm is proposed to separate the direct current components and a windowed limited impulse response algorithm is used to filter out the high-frequency noise.

Therefore, the application of the Mean Filter Algorithm implies that the uniform area is under processing: y ¯ β out = ∑ i = 0 N x βi / N, N = 17, (10).

The structure of Equation 11 can be illustrated using an example: if the Mean Filter Algorithm was selected for application instead of fuzzy rules 2, 3, 4, and 5, the sensitive parameter α = 0.125 used for the algorithm describes that the t and t + 1 frames are closely related.

Thus, the use of the Mean Weighted Filtering Algorithm is proposed.

This methodology was developed to effectively identify uniform areas within the image to be processed with a fast processing algorithm, like the 'Mean Weighted Filtering Algorithm' described below.

The proposed fast filtering procedure is carried out using the following methodology under an 'IF-OR-THEN-ELSE' condition: IF (θ1ANDθ3ANDθ4ANDθ6 ≥ τ1) OR (θ0ANDθ2ANDθ5ANDθ7 ≥ τ1) THEN the Mean Weighted Filtering Algorithm, ELSE the Spatial Filtering Algorithm (where τ1 is a threshold defined as 0.1).

If the Mean Weighted Filtering Algorithm [25] is selected, one processes the pixel's intensity components of the 3 × 3 window sample using Equation 2. This procedure is used because the angle deviation between the pixels is very small, which could indicate a uniform region where it is likely that there are no edges and details which may be softened.

Its focus on incorporating popularity within its hotel filtering algorithm means it can surface hotels that are "trending" in a particular destination — i.e. by looking at things like booking velocity, and traffic to particular hotels' websites.

Figure 4 The CRLB and the average mean-square error of the particle filtering algorithm (the number of particles, noise covariance matrix Σ = I).