AUTOMATIC DETECTION AND LABELLING OF PHOTOGRAMMETRIC CONTROL POINTS IN A CALIBRATION TEST FIELD
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
Abstract. In this work, a new method is developed for the automatic and accurate detection and labelling of signalized, un-coded circular targets for the purpose of automated camera calibration in a test field. The only requirements of this method are the approximate height of the camera, an approximate range of orientations of the camera, and the object-space coordinates of the targets. In each image, circular targets are detected using adaptive thresholding and robust ellipse fitting. Labelling of those targets is performed next. First, the exterior orientation parameters of the image are estimated using a one-point pose-estimation approach, where a list of possible orientation and target labels are used, along with height, to calculate the camera position. The estimated position and orientation of the camera combined with the interior orientation parameters (IOPs) are then used to back-project the known object-space coordinates of the targets into the image space. These targets are then matched against the targets detected in the image, and the list entry with the best fit is chosen as the solution. This resolves both the detection and labelling of the targets, without the need for any coded targets or their associated software packages, and each image is solved independently allowing for parallel processing. This process accurately labels 92–97% of images, with average accuracy rates of 97% or better, and average completeness rates of 70–95% in imagery from the three cameras tested. The cameras were calibrated using observations from the detection and labelling process, which resulted in sub-pixel root mean square (RMS) values determined for the pixel space residuals.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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