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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 width (thickness) of the border (default is 1px). style The border's style solid (default if the parameter is not used), dotted, dashed, double, groove, ridge, inset or outset. style2 Additional CSS properties can be used in this template. color The border's color (default #ddd, otherwise recommend a named color).
The nearest border pixels are conceptually extended as far as necessary to provide values for the convolution. Corner pixels are extended in 90° wedges. Other edge pixels are extended in lines. Wrap The image is conceptually wrapped (or tiled) and values are taken from the opposite edge or corner. Mirror The image is conceptually mirrored at ...
This template can be used to put a frame around a picture (or anything else). The frame is the same as in [[Image:Foo|thumb]] or [[Image:Foo|frame]], but without displaying a caption.
The algorithm looks for all nodes in the array that are connected to the start node by a path of the target color and changes them to the replacement color. For a boundary-fill, in place of the target color, a border color would be supplied.
After the clusters are located, typically the points in each cluster are averaged to obtain the representative color that all colors in that cluster are mapped to. The three color channels are usually red, green, and blue, but another popular choice is the Lab color space, in which Euclidean distance is more consistent with perceptual difference.
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 ...
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