Project 1: Noise & Form: Discovering Patterns
Emily Rabinowitz
For this project, I built myself a set of conceptual rules analogous to a few different findings in The Recognition of Faces by Harmon.
On sketch artist's process: "Few observers, unless they are specially trained, can give satisfactory clues to appearance in words. Most can point to features similar to those they remember, however and that is how the reconstruction artist usually begins."
To give clues to a criminal's appearance in words is like trying to paint photoshop algorithms by hand. Instead, I chose to "recognize" the "features" or the thumbprints of the transformations applied to the photos.
On perception: "An interesting and provocative characteristic of block portraits is that once recognition is achieved more apparent detail is noticed. It is as though the mind's eye superposes additional detail on the coarse optical image. Moreover, once a face is perceived it becomes difficult not to see it..."
Once recognition of the algorithm's visual patterns were made, I made sure to accentuate those patterns and only add to, not diminish, the level of detail that I could uncover.
More on perception: "...faces can be recognized as well as discriminated. It is possible not only to tell one from another but also to pick one from a large population and absolutely identify it, to perceive it as something previously known."
In order to accentuate found patterns, I used my recognition of hue discrimination. By understanding the differences in hue among various shapes, I could choose the hues that i wished to manipulate discretely.
More on sketch artist's process: "The first attempt, although obviously resembling the original photograph, differed from it in the depiction of important features and proportions. When limited feedback was allowed, however, there was rapid improvement."
The feedback I am allowing to influence my pieces are the "corrections" made by photoshop. This includes transformations such as level adjustments, and curves, and viewing the histogram. At times I chose to purposefully rebel against these corrections, such as making drastic changes in concavity on "curves" for Pattern17. Other times, I chose to follow photoshop's advice on how to best balance the photo, such as in Pattern13, where I adjusted levels and curves constantly to make the range of the grayscale as balanced as possible.
My goal is to discover the visual patterns of the algorithms encoded in photoshop while making transformations on pixels.
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Pattern11
Original
Image Size 20x20 px (Nearest Neighbor)
Image Size 2000x2000 px (Bicubic Smoother)
Hue/Saturation RGB saturation +92
Noise: Median 100 px
Hue/Saturation RGB saturation +49; Greens sat. +47; Reds sat. -43; Yellows sat. -21
Highpass 4.6 px
Hue/Saturation Greens, Cyans, Blues, Yellows, Reds saturation +100
Levels 0 2.21 161; output 255 0
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Pattern12
Original
Image Size 30x10 px (Nearest Neighbor)
Image Size 3000x1000 px (Bicubic Sharper)
Noise 33.03% (Uniform)
Noise: Median 3 px
Hue/Saturation Cyans sat. +100; Magentas sat. +62; Reds sat. +100 lightness +74
Levels 0 2.10 255
Curves RGB output:61 input:56; Blue output:164 input:181; Red output:138 input:107
Image Size 3000x3000 px (Bilinear)
Highpass 90.9 px
Noise: Median 90 px
Levels 77 .56 190
Vibrance -25
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Pattern13
Original
Levels 0 1.43 207
Levels 42 1.23 255
Hue/Saturation RGB saturation +90
Noise: Median 85 px
Levels RGB 5 .95 255; Green 0 1.96 244
Curves RGB output: 234 input:203; Red out:155 in:155; Green out:79 in:17; Blue out:253 in:214
Noise: Median 40 px
Highpass 11.5 px
Highpass 11.5 px
Hue/Saturation Cyans sat. +100; Greens sat +100 lightness -100; RGB sat. -100
Image Size 1067x600 px (Bicubic)
Noise: Median 1 px
Levels 51 1.09 255
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Pattern17
Original
Image Size 16x4 px (Nearest Neighbor)
Image Size 1600x1600 px (Bicubic Smoother)
Hue/Saturation RGB sat. +94
Noise: Median 93 px
Hue/Saturation saturation RGB +35; Yellows +51; Greens +65; Cyans +48; Blues +55; Magentas +54
Highpass 90.9 px
Hue/Saturation saturation Greens +52; Cyans +100; Yellows +25; Reds +73; Magentas +78
Curves RGB output:170 input:101
Noise: Dust & Scratches 5 px
Levels 0 .54 255
Hue/Saturation Blues hue +118 sat. +100; Greens hue -100; Cyans hue +15; Magentas hue +82 sat. +100 lightness +21
Highpass 250.0 px
Highpass 250.0 px
Highpass 250.0 px
Highpass 250.0 px
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Pattern18
Original
Image Size 3x3 (Nearest Neighbor)
Image Size 3000x3000 (Bicubic Sharper)
Levels 0 .91 241
Hue/Saturation saturation Magentas +91; Blues +92; Cyans +88; Yellows +82; Reds +93
Highpass 32.8 px
Levels 127 1.00 134
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Techniques:
Once an image is thoroughly destroyed and manipulated, to capture the underlying patterns adjust specific saturation levels, send the image through a highpass filter, and adjust the levels.
Median is a good transformation to use to add patterns to an image with little information.
Curves is a good tool to sculpt the banding of discrete hue families.
Additional discoveries:
Increasing the size of Pattern16 (not shown) added an interesting border that did not exist in the smaller image.