Why this work is in the frame
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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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it