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Record W2030907689 · doi:10.1142/s0219467807002829

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

2007· article· en· W2030907689 on OpenAlexaff
Meru Brunn, Mário Costa Sousa, Faramarz Samavati

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2007
Admission routes1
Has abstractyes

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