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By clustering the pixels in an image region into a number of sets according to their semantic meanings instead of using a regular division, it makes better use of the spatial information when constructing the local histograms.
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The candidate regions were first determined by clustering the brightest pixels in retinal images.
They apply a fuzzy clustering algorithm to cluster the pixels.
Methods: We segment pixels by clustering their mass spectra.
The PCA results were confirmed by clustering.
This is likely due to the fact that INEGI maps were generated with a classification approach, detecting larger areas of change formed by clusters of pixels that share similar characteristics and attributes.
The total area of GluRIIA-, Pand and BRP-positive clusters were defined by the pixels with an intensity above a threshold set arbitrarily, and were normalized by the total area of muscles.
K-means clustering iteratively updates the pixels and centroids of the two clusters until the sum of distances from all the pixels in each cluster is minimized.
The final segmentation is obtained by classifying the pixels into different pixel classes.
According to the pixels clustered by superpixels, we give initial depth values.
To generate the arbitrary number of patterns using definition, certain features are assumed for each 64-pixel pattern such that each is regular (i.e., bounded by straight lines), clustered (i.e., the pixels are connected), and boundary-adjoined.
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