Sentence examples for a spatial segmentation from inspiring English sources

We have recently proposed a spatial segmentation method solving this important issue, where edge-preserving spatially-adaptive denoising (Grasmair, 2009) is applied to gray-scale images of selected masses prior to clustering (Alexandrov et al., 2010).

MANOR combines a spatial segmentation procedure with a two-dimensional Loess regression and is optimized to preserve the true biological signal when correcting for spatial biases.

In general, the evaluation of a spatial segmentation remains an important and unsolved problem in imaging mass spectrometry data processing, where no reference or simulated data are provided yet.

A "spatial segmentation" algorithm has also been developed to account for array CGH-specific spatial effects designated "local spatial biases", where clusters of spots show a shift in signal, and "continuous spatial gradient", where there is a smooth gradient in signal across the array [52].

This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability.

ICM [ 23] follows the two assumptions made about spatial segmentation: a pixel's gray scale value g x is dependent only on its label l x and not the values of other pixels given the label l(x); the ideal label map L* is a realization of a locally dependent Markov Random Field with distribution p(x).

Concerning the second problem, the spatial segmentation of an IMS dataset, several approaches have been proposed.

Two unsupervised problems are currently considered in IMS data processing: (i) data representation using principal component analysis (PCA) and its variants (Klerk et al., 2007), the technique standardly used for processing SIMS data, and (ii) spatial segmentation of an IMS dataset by means of spectra (or pixels) clustering (Alexandrov et al., 2010; Deininger et al., 2008; McCombie et al., 2005).

In this paper, a novel object tracking based on spatial segmentation is proposed to handle the problem of drastic appearance changes of the deformable object.

This aerial derived data was obtained using spectral and spatial segmentation and reconciliation procedures that segmented hemlock "blobs" from the forest canopy image.

Our spatial segmentation methods can be applied for segmenting other hyper-spectral or multi-channel data, for example for terahertz imaging (Brun et al., 2010), or hyper-spectral imaging (Tarabalka et al., 2010) like that one used in German Hyperspectral Satellite Mission (Stuffler et al., 2007).