MétaCan
Menu
Back to cohort
Record W2030907689 · doi:10.1142/s0219467807002829

CAPTURING AND RE-USING ARTISTIC STYLES WITH REVERSE SUBDIVISION-BASED MULTIRESOLUTION METHODS

2007· article· en· W2030907689 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

VenueInternational Journal of Image and Graphics · 2007
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSubdivisionRendering (computer graphics)Computer scienceMultiresolution analysisClassification of discontinuitiesArtificial intelligenceNon-photorealistic renderingComputer visionComputer graphics (images)Point (geometry)MathematicsGeometryGeography

Abstract

fetched live from OpenAlex

We describe a multiresolution method for rendering curves that is based on exact reproduction of artistic silhouettes and line hand-gesture styles. Using analysis based on reverse subdivision, we extract examples from both scanned images of line-drawn artwork and interactively-sketched input and apply these styles to the arbitrary strokes of new illustrations. Our algorithms work directly with the extracted discrete point data using fast and simple local and global multiresolution filters, and we support the use of styles with gaps or discontinuities. Our results show how this technique can capture the complex contour drawings of landscape elements, allowing users without drawing skills to easily reproduce them.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.368
Teacher spread0.343 · 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