Algorithmic Shape Collages

Interessantes Paper der Uni Hong Kong über automatisierte Shape Collagen (mit geilem Titel auch): Pyramid of Arclength Descriptor for Generating Collage of Shapes. (via Eric Arnebäck)

Ich hab' solche Dinger früher ab und zu als Illu für die Magazin-Beilage der Zeitung gebaut, bei der ich damals arbeitete und manuell sind solche Collagen ein ziemlicher Aufwand (wenn man Abstände und Skalierung der einzelnen Formen halbwegs gleichförmig haben will). Demnächst dann per Script.

This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary to reduce gaps and overlaps in the collages. Second, the search space in identifying a good coupling of shapes is highly enormous, since arranging a shape in a collage involves a position, an orientation, and a scale factor. Yet, this matching step needs to be performed for every single shape when we pack it into a collage.

Existing shape descriptors are simply infeasible for computation in a reasonable amount of time. To overcome this, we present a brand new, scale- and rotation-invariant 2D shape descriptor, namely pyramid of arclength descriptor (PAD). Its formulation is locally supported, scalable, and yet simple to construct and compute. These properties make PAD efficient for performing the partial-shape matching. Hence, we can prune away most search space with simple calculation, and efficiently identify candidate shapes. We evaluate our method using a large variety of shapes with different types and contours. Convincing collage results in terms of visual quality and time performance are obtained.