holger lippmann

image-noise-grid

image-noise-grid (2014-2016)
using perlin noise to generate an XY dot matrix, reading out layered image color maps with either random distortion of color and/or appearance.
random and/or manual layering and mixing of different color-map data…

made with code (www.processing.org)

image-noise-grid 1 (2014)

image-noise-grid 2 (2015)

image-noise-grid 3 (2015)

image-noise-grid 4 (2016)

image-noise-grid 5 (2016)

Published on November 26, 2014 10:28 am.
Filed under: 2014, 2015, 2016, landscape, LATEST, structure Tags: algorithmic, generative, perlin noise, processing

spinners

the spinners afternoon 2, 2010

the spinners series is another particle system based processing work.
different keyboard and/or mouse inputs are used for manipulation while recording.

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Published on June 29, 2010 2:29 pm.
Filed under: 2010, structure

spiral scratch

spiral scratch, 2009
made with processing
vector for large print

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Published on November 15, 2009 12:18 pm.
Filed under: 2009, structure

c-flower

Effervescent-Everpresent
2008/09
pigment on canvas
200x300cm ( 3 parts)

c-flower is based on a bezier vector curve forming a flower like shape by by rotating it and deforming it by random and/or noise values.
the coloring is made with read out of predefined color map arrays and layering.

Published on June 29, 2009 3:38 pm.
Filed under: 2009, 2010

curve drawer 1

schorfheide, 2010

the curve drawer series is based on a vector curve function which reads out a color map or map array and draws in differently occillation structures over the stage, controlled by different mouse action.

Published on April 17, 2009 4:18 pm.
Filed under: 2008 and older, 2009, landscape

swirl

swirl series, 2009
based on a rotating bezier curve and color map read out.

Published on February 9, 2009 10:11 am.
Filed under: 2008 and older, 2009

de jong

de jong series, 2008

these works are based on the simple Peter de Jong map equations:
x’ = sin(a * y) – cos(b * x)
y’ = sin(c * x) – cos(d * y)

For most values of a,b,c and d the point (x,y) moves chaotically. The resulting image is a map of the probability that the point lies within the area represented by each pixel. As you let it render longer it collects more samples and this probability map and the image becomes more accurate.
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Published on January 8, 2009 9:22 pm.
Filed under: 2008 and older, structure