Drawing accuracy measured using polygons
Bibliographic record
Abstract
The study of drawing, for its own sake and as a probe into human visual perception, generally depends on ratings by human critics and self-reported expertise of the drawers. To complement those approaches, we have developed a geometric approach to analyzing drawing accuracy, one whose measures are objective, continuous and performance-based. Drawing geometry is represented by polygons formed by landmark points found in the drawing. Drawing accuracy is assessed by comparing the geometric properties of polygons in the drawn image to the equivalent polygon in a ground truth photo. There are four distinct properties of a polygon: its size, its position, its orientation and the proportionality of its shape. We can decompose error into four components and investigate how each contributes to drawing performance. We applied a polygon-based accuracy analysis to a pilot data set of representational drawings and found that an expert drawer outperformed a novice on every dimension of polygon error. The results of the pilot data analysis correspond well with the apparent quality of the drawings, suggesting that the landmark and polygon analysis is a method worthy of further study. Applying this geometric analysis to a within-subjects comparison of accuracy in the positive and negative space suggests there is a trade-off on dimensions of error. The performance-based analysis of geometric deformations will allow the study of drawing accuracy at different levels of organization, in a systematic and quantitative manner. We briefly describe the method and its potential applications to research in drawing education and visual perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".