A Combined Extended Circular Image And Spatial Occupancy Approach To 2-D Contour Matching.
Bibliographic record
Abstract
In this paper we propose representa- tions of two-dimensional curves that capture curve orientation and spatial occupancy, and we demon- strate their use in two-dimensional contour match- ing. To explicitly represent curve orientation, we generalize the notion of extended circular image to a non-convex curve, by representing such a curve as the sequence of the extended circular images of its convex and concave parts. Evaluating the sim- ilarity of two curves can then be reduced to eval- uating the similarity of corresponding segments by directly correlating their extended circular im- ages. To explicitly represent spatial occupancy in a manner that can be used in shape matching, we blur the two-dimensional binary image obtained from the curve. Two curves that are similar in both shape and size and optimally aligned with respect to each other in both position and orienta- tion will then result in a value close to one of the correlation coefficient obtained from the respec- tive binary images. Empirical results using real data are collected and analyzed, to determine the strength of the resulting technique in practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".