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Record W3183827559 · doi:10.1111/cgf.14343

PosterChild: Blend‐Aware Artistic Posterization

2021· article· en· W3183827559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Graphics Forum · 2021
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPalette (painting)Computer scienceComputer graphics (images)Variety (cybernetics)Computer visionArtificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

Abstract Posterization is an artistic effect which converts continuous images into regions of constant color with smooth boundaries, often with an artistically recolored palette. Artistic posterization is extremely time‐consuming and tedious. We introduce a blend‐aware algorithm for generating posterized images with palette‐based control for artistic recoloring. Our algorithm automatically extracts a palette and then uses multi‐label optimization to find blended‐color regions in terms of that palette. We smooth boundaries away from image details with frequency‐guided median filtering. We evaluate our algorithm with a comparative user study and showcase its ability to produce compelling posterizations of a variety of inputs. Our parameters provide artistic control and enable cohesive, real‐time recoloring after posterization pre‐processing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.262
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it