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The "voids-and-cluster" method gets its name from the matrix generation procedure, where a black image with randomly initialized white pixels is gaussian-blurred to find the brightest and darkest parts, corresponding to voids and clusters. After a few swaps have evenly distributed the bright and dark parts, the pixels are numbered by importance.
It helps the user to distinguish the selection border from the image background by animating the border. The border is a dotted or dashed line where the dashes seem to move slowly sideways and up and down. This creates an illusion of ants marching in line as the black and white parts of the line start to move.
The general method of scaling remains the same, whereby the 8 corners of the cuboid do not scale, similar to the 4 corners present in 9-slice scaling. The central core scales in 3 dimensions. The 6 faces of the cuboid scale in 2 dimensions. The remaining 12 edge pieces scale in 1 dimension. All of which are relative to the actual scaling applied.
The term dither was published in books on analog computation and hydraulically controlled guns shortly after World War II. [1] [2] [nb 1] Though he did not use the term dither, the concept of dithering to reduce quantization patterns was first applied by Lawrence G. Roberts [4] in his 1961 MIT master's thesis [5] and 1962 article. [6]
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
In computer graphics, color quantization or color image quantization is quantization applied to color spaces; it is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image.
Generally hierarchical algorithms produce more in-depth information for detailed analyses, while algorithms based around variants of the K-means algorithm are more efficient and provide sufficient information for most purposes. [1]: Ch.14 These algorithms can further be classified as hard or soft clustering algorithms.
Let be a metric space with distance function .Let be a set of indices and let () be a tuple (indexed collection) of nonempty subsets (the sites) in the space .The Voronoi cell, or Voronoi region, , associated with the site is the set of all points in whose distance to is not greater than their distance to the other sites , where is any index different from .