Sentence examples for kernel standard from inspiring English sources

This gives the maximum time step as a function of the fluid viscosity, the flow velocity scale and the SPH discretization size (kernel standard deviation).

The software layers consist of a custom Linux kernel, standard system libraries, a modified TrouSerS TSS, and the TrustCAM software framework.

In the simulation, blurred noisy projection images of a 3D numerical breast phantom were generated by convolving their original (or exact) version by a designed 2D Gaussian filter kernel (standard deviation=2 in pixel unit, kernel size=11×11), followed by adding Gaussian noise (mean=0, variance=0.05), and deblurred by using the algorithm before performing the DBT reconstruction procedure.

The resulting attenuation maps are smoothed using a Gaussian filter with a kernel standard deviation of 2 voxels (1.172 × 1.172 × 2.5 mm) to approximate the PET's point spread function (PSF), and resampled to the PET's discretisation grid.

Image reconstruction was performed using two kernels: the standard kernel for the standard algorithm and the lung-enhanced kernel for the high-resolution algorithm.

Examples include diffusion kernels or standard kernels such as linear or Gaussian kernels [ 2] for encoding the similarity between data objects x i 1 and x i 2. These data kernels are, in turn, embedded in pairwise kernels, as described in the previous section.

It is possible to further combine these types of pairwise kernels with other standard kernels, for example, Gaussian kernels or kernels based on polynomials; for example, (9) K = K x i 1, x i 3 + K x i 2, x i 4 + r d.

The weight function is defined as the nonlocal means weight function: omega left x,yright)=exp left{-frac{G_aast {leftVert fleft(x+cdot right -fleft y+cdot right -fleft y+cdot{2{h}^2}right}, (8)wherightVert the Gaussian kernel with standard deviation a, h is the filtering parameter related to the standard variance of the noise, and the ⋅ in f(x + ⋅) denotes a square patch centered by point x.

where Gσ is a 2D Gaussian kernel with standard deviation σ, ∇ and * present gradient and convolution operators, respectively.

where K σ is a Gaussian kernel with standard deviation σ, and f1 (x) and f2 (x) are two smooth functions that approximate the local image intensities inside and outside the contour, respectively.

A typical external force for a gray-scale image I is Eext = -|∇G σ *I|, where G σ is the Gaussian kernel with standard deviation σ and where * denotes convolution.