Bibliographic record
Abstract
Detection of straight lines in an image is a fundamental requirement for many applications in computer vision. We formulate the straight line detection task as an energy minimization problem. This formulation helps the detection of lines in a global manner in contrast to the local detection methods used in conventional algorithms. As a result the proposed straight line detection algorithm can handle virtually co-located straight lines, slightly curved lines and edge linking in a unified manner. In addition, due to its the global nature, the algorithm is not deceived by image noise giving rise to spurious line segments. Therefore, the proposed algorithm can robustly detect straight lines. The main component of the algorithm is formulating the energy to be minimized. The contribution to this energy function is less at a pixel which is a good candidate to be a member of an existing line segment depending on the directional gradients. A pixel choosing a part of a line segment is costly, but not impossible. This energy optimization is done using dynamic programming snakes. Since the algorithm is a global one and since no gradient calculations are used for local motion of nodes, our algorithm is robust. However, the optimization process takes a longer time than the existing straight line detection algorithms. Results are given for detecting straight lines in indoor environments
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".