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Record W2188906995

An improved method for generating triangle-mesh models of images

2014· article· en· W2188906995 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

VenueCanadian Conference on Computational Geometry · 2014
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputationComputer scienceMargin (machine learning)AlgorithmTriangulationNoise (video)Triangle meshSignal-to-noise ratio (imaging)Artificial intelligenceImage (mathematics)MathematicsComputer graphics (images)Polygon meshTelecommunicationsGeometry
DOInot available

Abstract

fetched live from OpenAlex

In earlier work, Yang et al. proposed a highly-effective technique for generating triangle-mesh models of digital images, known as the error diffusion (ED) method. In this paper, we propose a modified version of the ED method that better exploits triangulation connectivity. Through experimental results, our proposed method is shown to generate image approximations of substantially higher quality than those obtained with the ED scheme, by an average margin of about 3 decibels in terms of the peak-signal-to-noise ratio. Moreover, this improvement in quality comes at a relatively modest computational cost, with the proposed method typically requiring only a few seconds of computation time.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.326
Teacher spread0.283 · 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