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After computing the sensitivity map, we smooth the sensitivity distribution using a uniform kernel and cluster the sensitivities into two Gaussian distributions using the expectation-maximization (EM) algorithm [31].
In this work all the segmentation experiments were performed by using a uniform kernel.
This corresponds to using a uniform kernel of width h in Equation (6) and apply it to every h position.
For that, the ipsilateral eye magnitude map was first smoothed to reduce pixel shot noise by low-pass filtering using a uniform kernel of 5x5 pixels, and then thresholded at 30% of peak response amplitude to eliminate the background noise.
For that, the ipsilateral eye magnitude map was first smoothed to reduce pixel shot noise by low-pass filtering using a uniform kernel of 5 × 5 pixels, and then thresholded at 30%% of peak response amplitude to eliminate the background noise.
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In the first step, random noise in a signal sequence is suppressed using convolution with a uniform kernel that has a half-width of ∼1/10 of the fundamental period.
For descriptive purposes, we first estimated the average percent error as a function of infarct volume using the non-parametric regression method of Racine and Li (2004) with a uniform kernel type and a uniform bandwidth selected by cross-validation.
The quantity n x /n (h d c d) is the kernel density estimate f̂ U (x) (where U means the uniform kernel ) computed with the hypersphere S h(x), and thus we can write expression (4) as: which yields, Expression (7) shows that an estimate of the normalized gradient can be obtained by computing the sample mean shift in a uniform kernel centered on x.
We use a uniform distribution.
The spot enumeration was done with a kernel density estimation based algorithm [15] using a Gaussian kernel.
Mutual information was computed using a Gaussian Kernel estimate.
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