New York Times

CNN

We've been investigating methods for the extraction and analysis of color sets from digital images. Here we are comparing the 64 most dominant colors from a series of online news sources on April 1, 2008.

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(A) 100%		(B) 90%		(C) 80%		(D) 70%		(E) 60%		(F) 50%		(G) 40%		(H) 30%		(I) 20%

This is another test run of our Koala script. On this version we've added randomness as a function to each component. Based on a given percentage, a component has a certain likelihood of either being on or off, in addition to all other assigned variables. As they increase, the resultant absences produce decreasing legibility in the organization of the field.

Koala V3 | Frame 001		Koala V3 | Frame 050		Koala V3 | Frame

We've finished developing our Koala script for the time being. In this final iteration interrelationships are further refined, and new limits on scale, color, and shape are introduced. The result is now a softly modulated series of patterns that register difference both at the local and global scale but to very different effects.

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We are currently developing techniques to produce topologies with reciprocal parametric relationships between form and perforation. The above renderings show a Wankel inspired torus with three slightly varied strategies.

Blue Gold | Detail

BlueGold explores a further developed version of the script we developed for Koala. Here it is applied to a three dimensional fabric of linked components wherein rotation, material, and color are implemented as additional variables producing variation across a surface at the level of scale, reflection, porosity, and thickness.

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Koala | Detail A1		Koala | Detail A2		Koala | Detail B1		Koala | Detail B2

Koala is a MEL script we're developing that enables incremental parametric variation in a field of components. In this first test, a field of circles achieves scalar variation by means of the relationship between each component and the distance between two adjustable points in space.

These images were generated in 2D and are presently effective primarily at the graphic level. The parametric intelligence within this type of variation, however, enables higher level reciprocal relationships between components in a field and thus a more sophisticated model of variation than is generally deployed.

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