Robust and efficient skeletal graphs
Bibliographic record
Abstract
There has recently been significant interest in using representations based on abstractions of Bhum's skeleton into a graph, for qualitative shape matching. The application of these techniques to large databases of shapes hinges on the availability of numerical algorithms for computing the medial axis. Unfortunately this computation can be extremely subtle. Approaches based on Voronoi techniques preserve topology but heuristic pruning measures are introduced to remove unwanted edges. Methods based on Euclidean distance functions can localize skeletal points accurately, but often at the cost of altering the object's topology. In this paper we introduce a new algorithm for computing subpixel skeletons which is robust and accurate, has low computational complexity and preserves topology. The key idea is to measure the net outward flux of a vector field per unit area, and to detect locations where a conservation of energy principle is violated. This is done in conjunction with a thinning process applied in a rectangular lattice. We illustrate the approach with several examples of skeletal graphs for biological and man-made silhouettes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".