Mitchell’s Best-Candidate II

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Mitchell’s Best-Candidate II

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Last edited Feb 9, 2016

Created on Jun 4, 2014

The animation demonstrates Mitchell’s best-candidate algorithm, a method for distributing sample points with blue-noise spectral characteristics to minimize aliasing. Each new point is chosen from ten gray candidate points, with the red best candidate selected as the one farthest from all existing black points. The visualization shows the search radius from each candidate to its closest existing point as a circle and line. It uses d3.v3 with SVG transitions and a d3.geom.quadtree for efficient nearest-neighbor queries.

AI-generated description

An animation of Mitchell’s best-candidate algorithm, which produces samples with blue-noise spectral characteristics that are useful for minimizing aliasing. Unlike uniform random sampling, best-candidate samples are more evenly distributed, with fewer samples close together. (A similar, but more efficient, algorithm is poisson-disc sampling.)

For each new sample, the best-candidate algorithm generates a fixed number of candidate samples, shown in gray. Here, 10 candidates are generated. The best candidate, shown in red, is the one that is farthest away from all previous (non-candidate) samples.

gpl-3.0 Licensed

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