PLSD: A Perceptually Accurate Line Segment Detection Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most existing line segment detection methods suffer from the over-segmentation phenomenon. An improved line segment detection method is presented in this work, which can generate more and longer line segments, yet still accurately reflect the structural details of the image. Line segment grouping, line segment validation and a multiscale framework are adopted to reach this end. Specifically, smart grouping rules are introduced to locate potential homologous line segments (derived from the same boundaries). Novel merging criteria based on Helmholtz principle is then used to evaluate the meaningfulness between separate line segments and their merged ones. The improved multiscale framework facilitates line segments merging in detection and post-detection processes, yielding more high-quality line segments. Finally, the proposed method is compared with four leading methods on the famous public dataset, YorkUrban-LineSegment. Experimental results demonstrate that the method has good continuity and outperforms the leading methods in F-measure.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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