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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.
Top: Traditional scaling, corners are distorted. Bottom: 9-slice scaling, corners aren't distorted. 9-slice scaling (also known as Scale 9 grid, 9-slicing or 9-patch) is a 2D image resizing technique to proportionally scale an image by splitting it in a grid of nine parts.
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.
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.
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A plausible solution would be to take the 2 digit number (say, 4.8) and round it one direction or the other. For example, it could be rounded to 5 one time and then 4 the next time. This would make the long-term average 4.5 instead of 4, so that over the long-term the value is closer to its actual value.
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.
The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.