Detecting viewer-perceived intended vector sketch connectivity
Bibliographic record
Abstract
Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually, or even semi-manually, correcting drawings to generate correctly connected outputs is tedious and highly time consuming. We propose a novel, robust algorithm that extracts viewer-perceived stroke connectivity from inexact free-form vector drawings by leveraging observations about local and global factors that impact human perception of inter-stroke connectivity. We employ the identified local cues to train classifiers that assess the likelihood that pairs of strokes are perceived as forming end-to-end or T- junctions based on local context. We then use these classifiers within an incremental framework that combines classifier provided likelihoods with a more global, contextual and closure-based, analysis. We demonstrate our method on over 95 diversely sourced inputs, and validate it via a series of perceptual studies; participants prefer our outputs over the closest alternative by a factor of 9 to 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".