Robust Detection of Corners and Corner-line Links in Images
Why this work is in the frame
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Bibliographic record
Abstract
We define corner points in an image as the intersections among detected straight line segments, and propose an algorithm that detects corners from such a definition. Our corner detection algorithm CLDC then makes use of the LDC (Line Detection using Contours) algorithm from, which outputs the list of all detected line segments together with their endpoints. Each line segment is extended in a post-processing step. CLDC (Corners from LDC) then finds corners in O((n + I)log n) time, where n and I are the number of endpoints the intersections of line segments, respectively. Detected corners are linked via line segments that define them. Such an output of the corner detection algorithm is a novel concept. The algorithm is comparable in time complexity with other algorithms, while providing more information about the line segments in the image. CLDC is robust to image transformations, such as rotation and translations. Our CLDC is compared to some existing algorithm, and its advantages are demonstrated.
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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 | 0.000 |
| 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