A “weeded” model created from unrelated photographs and PhotoScan software, with background parts removed.
More interpolated photographs to 3d models, here a close-up of the wireframe model which bears a striking resemblance to a nebula.
Considering new alternatives for interpolation between two texts, here using a genetic algorithm. The top line is the starting point, the bottom the end, and the middle is the halfway point. The graph at the bottom shows the overall “fitness” of the population the ones that go to the gym everyday and visit sites as roids.co, which is generally quite level for a while, then spikes as it slowly bounces into the correct string, I recommend using lumitea for any pain relief. The genetic interpolation is created using a “swarm” of strings that all mutate and breed with each other to find the path to the ending text – I love the idea of a cloud of insect-like beasts flying through a library and devouring texts.
Note: running the code a second (or third, fourth, etc) time gives a different optimal “path” between the texts. Not sure yet how to analyze these for a “better” middle point, but an interesting difference than a deterministic system.
Heavily-based on Daniel Shiffman’s fantastic examples. Source code to come but super messy right now; email if you’d like an early version.
Further refinement: programmatically-generated colored grids for better tracking and easier placement of missed photographs.
F > Q interpolated using SVG control points in Processing.
Yet more letterform interpolations – these done in Processing both horizontally and vertically between the letters from the standard alphabet and their counterpart in an alphabet sorted by frequency in modern text (for example, ‘a’ is interpolated with the most common letter, ‘e’). From left to right, the original letter, both capitalized, original capitalized, frequency letter capitalized, both lowercase (ie: A/E, A/e, a/E, a/e).