Accurate Measurement of Surface Grid Intersections From Close-Range Video Sequences
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
A novel approach for high-speed measurement of surface grid line intersection points across multiple video frames is described. Grids are printed or etched onto otherwise featureless surfaces for applications that include 3-D surface reconstruction, sheet metal surface strain measurement, and others. To achieve the necessary subpixel location accuracy, close-range imaging is used with data collected by a hand-guided digital video camera mounted on a portable articulated arm coordinate measuring machine. Grid extraction is based on ridge detection in a parallelized scale space, implemented with a 480-core graphical processing unit (GPU). The close-range narrow-depth-of-field focus variations within the video sequence are intrinsically handled by the scale space. Ridge linking, filtering, and parabola fitting are used to accurately extract the grid intersection points. While computationally intensive, experimental implementation using the parallel GPU hardware has achieved sustained throughput exceeding 15 frames per second, with more than 100 intersections extracted per frame. Experimental results are presented for both synthetically generated and actual video sequences.
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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 | 0.001 | 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 | 0.001 |
| 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