Sentence examples for approximation of the image from inspiring English sources

Finally, the third class includes all the SfS methods that make an approximation of the image irradiance equation at each pixel composing the input image.

The L0-AbS method is based on a simple image model, which has two statistical parameters: α, controlling the amount of sparsity of the sparse approximation of the image in the representation domain, and σ r, which is the standard deviation of the sparse approximation from the actual vector of the image in the representation domain.

The energy functional E ψ PSF ϕ in (19) is in fact a square error of the approximation of the image I (x) by the PS function I ψ PS x, ϕ ; therefore, we call E ψ PSF ϕ the piecewise smooth fitting (PSF) energy.

Since the optimal shift is a continuous parameter as opposed to the grid of microscopy image, we use piecewise linear approximation of the image to achieve the estimate of the mean sarcomere length at subpixel resolution.

This new framework, however, improves greatly on previous work through the addition of shape and texture generation based on data obtained from an extensive database of segmented cells, the inclusion of more debris models, a more versatile method for object distribution, and an accurate approximation of the image depth of field, allowing for simulated focus stacks to be created.

Based on this framework, Chan and Vese [3] developed the frequently used variational segmentation model, which simplifies the Mumford-Shah model to the case of piecewise constant approximations of the image intensity.

Figure 12 Approximation of the phantom image: (a) Original phantom image, (b) Approximation using Haar wavelets (N d  = 3300 coefficients).

In Context-Based Decision (CBD) injection model, the weights are calculated locally between the MS band resampled to the scale of the PAN image and an approximation of the PAN image at the resolution of the MS bands.

Another comparative analysis was developed in [14], where a general image fusion (GIF) framework for pansharpening IKONOS images is proposed and the performance of several image fusion method is analyzed based on the way they compute, from the LR MS image, an LR approximation of the PAN image, and how the modulation coefficients for the detail information are defined.

7/9 5 8000 35.30 – 1.51 2.48 Figure 13 Approximation of the phantom image: (a) Approximation using the EPWT with Haar (N d  = 650coefficients), (b) Approximation using the easy path algorithm and DCT-based IPRM (N d  = 175 coefficients).

Its main idea is as follows: given an image I, by minimizing an energy functional to find a pair of (u,C) for partitioning the image into some disjoint sub-regions, u is an almost piecewise smooth approximation of the original image I, and C denotes the smooth and closed contour.