EXPERIMENTS ON CALIBRATING TILT-SHIFT LENSES FOR CLOSE-RANGE PHOTOGRAMMETRY
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
One of the strongest limiting factors in close range photogrammetry (CRP) is the depth of field (DOF), especially at very small object distance. When using standard digital cameras and lens, for a specific camera – lens combination, the only way to control the extent of the zone of sharp focus in object space is to reduce the aperture of the lens. However, this strategy is often not sufficient; moreover, in many cases it is not fully advisable. In fact, when the aperture is closed down, images lose sharpness because of diffraction. Furthermore, the exposure time must be lowered (susceptibility to vibrations) and the ISO increased (electronic noise may increase). In order to adapt the shape of the DOF to the subject of interest, the Scheimpflug rule is to be applied, requiring that the optical axis must be no longer perpendicular to the image plane. Nowadays, specific lenses exist that allow inclining the optical axis to modify the DOF: they are called tilt-shift lenses. In this paper, an investigation on the applicability of the classic photogrammetric model (pinhole camera coupled with Brown’s distortion model) to these lenses is presented. Tests were carried out in an environmentally controlled metrology laboratory at the National Research Council (NRC) Canada and the results are hereafter described in detail.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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