Sentence examples for local regularization from inspiring English sources

We illustrate the snake behavior on synthetic and real images, with global and local regularization.

Hence, we propose a stabilized formulation based on a local regularization of the fluid velocity along the tangential directions on the Neumann boundaries.

A new method based on local regularization has been proposed for data processing to achieve more accurate reconstruction of the peak intensities and thicknesses of chemiluminescent zones in flames.

They extract position invariant features using a sparse codebook on aligned images, and apply a local regularization framework on these features for automatic image annotation.

In System II, Ji et al. (2009) used dense SIFT feature descriptors that are converted into sparse codes to form a codebook to represent their aligned images, and proposed an elegant local regularization (LR) procedure for multi-label learning.

We propose, too, an approximation for the SV-matrix inversion, under the assumption of having a smoothly varying PSF field and enough regularization as to ensure its local regularized inverse also changes smoothly in the space.

where δ g represents the sum of the distances of the PTVs obtained after local weighting regularization to (x c,y c ), calculated independently for each vertex.

It was found experimentally that k could be the same for both the global and local weighting regularization approaches as no benefits were obtained with different k values.

The proposed local weighting regularization technique computes the distortion D l associated to each warped block as: δ l x, y = x - x c 2 + y - y c 2 (7) D l = MAD 1 + k δ l (8)  .

The local automatic regularization in LLARRMA implies a different perspective: that λ is a parameter intrinsic to, and only meaningful in the context of, a single LASSO path on a single (subsampled) realization of the data.

We demonstrate a principled approach, LASSO local automatic regularization resample model averaging (LLARRMA), that characterizes sensitivity of locus choice to sampling variability and uncertainty due to missing genotype data, and that provides LASSO shrinkage automatically regularized through either predictive- or discovery-based criteria.