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#patterns

11 posts6 participants0 posts today
Public
Risto A. Paju

I came across this halftoning idea sometime last week. While the idea was relatively easy to understand and fun to implement, I've spent quite a lot of time trying to make the result look nice.

In each row, the image is split into bins containing roughly the same sum of lightness value. This is nice to implement when the number of lines/bins is a power of 2, so we can recurse with a binary split. Thus the line density varies by average lightness. The problem is that density is considered along the x-axis. If things change a lot between rows, the lines get slanted, so they appear more dense. Here I've included some averaging between neighbouring rows to make thing a bit smoother.

I'm also including a fun glitch from the early tests. The line-density system includes the set of point coordinates and the graph structure (which point is connected to which). What happened here is my generic graph generator that simply finds the nearest neighbours of each point. So in the light areas that are compressed horizontally, the nearest neighbours were left and right.

#halftoneart#lineart#patterns
Public
Jörg Lehmann

Want to find patterns in bibliographic metadata? Want to perform bibliographic data science on the history of books?

Then feel free to use this dataset:

Metadata of the "Verzeichnis der im deutschen Sprachraum erschienen Drucke"
doi.org/10.5281/zenodo.15167938

This dataset consists exclusively of descriptive metadata of about 750.000 titles, which together form a retrospective German national bibliography of prints 1501-1800.

#ml #patterns #datascience

@cneud @stabi_berlin

ZenodoMetadata of the "Verzeichnis der im deutschen Sprachraum erschienen Drucke"This data publication was created with the intent to provide bibliographic and subject indexing metadata for research purposes and the development of AI applications. This data publication can be regarded as the German national bibliography of the period 1500–1800. It consists of two files: The first one provides all bibliographic master records pertaining to the German national bibliography which were edited and revised and electronically copied to the union catalogue K10plus, the shared database of the Bibliotheksservice-Zentrum Baden-Württemberg (BSZ) and the Verbundzentrale des GBV (VZG). The second one lists the unique identifiers (“Pica-Produktionsnummer”) of all master records available in the union catalogue K10plus which have been digitised, and it contains the links to their digital copies. The primary intention for this data publication was the provision of a large computationally amenable dataset exclusively consisting of bibliographic metadata to stimulate research and development of AI applications. In 2025, large (meta-)datasets from the field of historical cultural data are still missing. In this respect, the data publication provided here aims to fill a gap. The files are suitable for the computational use of digitised and born-digital collections according to the Collections as Data principles. The data publication was created by two librarians and a member of the team of the research project “Mensch.Maschine.Kultur – Künstliche Intelligenz für das Digitale Kulturelle Erbe” at Berlin State Library (SBB). The research project was funded by the Federal Government Commissioner for Culture and the Media (BKM), project grant no. 2522DIG002. The Minister of State for Culture and the Media is part of the German Federal Government.
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