Scale-space ridge detection with GPU acceleration
Why this work is in the frame
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Bibliographic record
Abstract
Imaging systems for computer vision play an important role in today's world. Typical computer vision systems operate on large scale scenes, where objects are relatively far from the camera and the depth of field in which objects appear focussed is large. Close-range camera systems, on the other hand, typically have a narrow depth of field. World features outside this depth of field are blurred, and in applications where poor data may not be re-acquired, a technique is required to reliably extract information from these images. Discrete scale-space feature detection techniques provide methods to extract features from these images, but bring with them a significantly higher computational workload compared with classical edge and ridge detectors. This paper presents the results from implementation of a discrete scale-space ridge detector with graphics processing unit (GPU) acceleration. This feature detector has been applied to close-range images of grids printed on sheet metal surfaces, and a speedup of one to two orders of magnitude is seen over a CPU-based implementation of the same feature detector.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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