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Where μ i is the mean of points in S i and k = 2.
where μ i is the mean of points in S i, is minimized [49,50].
An elementary mean of points x 1, …, x n ∈ I is the arithmetic mean 1 n ∑ i = 1 n x i ∈ I.
The non-local mean filter takes the mean of points whose Gaussian neighborhood resembles the neighborhood of a given pixel [4, 5].
The discrete basic φ-quasi-arithmetic mean of points (particles) x i with coefficients (weights) p i is the point M φ ( x i, p i ) = φ − 1 ( ∑ i = 1 n p i φ ( x i ) ) (4.1).
The points are divided into k clusters by iteratively calculating the mean of points (or centroid of points) as the new temporary center point within a given radius of the current temporal center point until the center point converges.
Similar(51)
where S i is the cluster i, X j is coordinate of sensor node j and m i is the coordinate of mean of point.
Post-treatments offer means of introducing point or incoherent line sources.
The setting of initial means of points is very important.
Figure 6 Example of the new means of points.
Because the means of points are changed, all sensor nodes are re-classified by executing Equations (9) and (10) iteratively to obtain the minimum average distance between the means of points and the sensor nodes for all clusters.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com