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Record W2134095301 · doi:10.1109/iv.2005.52

From Form to Content: Using Shape Grammars for Image Visualization

2006· article· en· W2134095301 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

VenueNinth International Conference on Information Visualisation (IV'05) · 2006
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsConcordia University
Fundersnot available
KeywordsVisualizationComputer scienceRule-based machine translationAlgebraic numberContent (measure theory)Identification (biology)Variation (astronomy)Theoretical computer scienceArtificial intelligenceAlgebra over a fieldComputer graphics (images)MathematicsPure mathematics

Abstract

fetched live from OpenAlex

The idea of superimposing geometric grids on images to visualize their content is not new. Leonardo Da Vinci used it, Durer used it, and Descartes pioneered the use of geometric grids to describe geometric content with algebraic equations. Shape grammars take the algebraic analysis of images to a new dynamic level. They permit the visualization of images in terms of construction processes: generators and relations, in the language of algebra. In this paper, we discuss some of the creativity involved in the identification of initial objects and rules for the analysis of both a Zillij mosaic and a Kuba cloth. We show that although conceptually similar, the processes are quite different for the two types of design. While Zillij mosaics are regular, Kuba cloths also involve scaling: the variation of the size of repeated sub-patterns within a defined space.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.953
Threshold uncertainty score1.000

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 communication0.0020.006
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.357
Teacher spread0.264 · 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