Closed iris, long exposure photography improves three-dimensional photogrammetric bone reconstructions
Bibliographic record
Abstract
• 3D photogrammetry allows digital reconstruction of objects requiring only a camera. • Digital reconstruction of reflective surfaces may be subject to distortion. • Digital reconstructions from high and low aperture pictures were experimentally compared. • High aperture, long exposure pictures reduced distortions and improved 3D photogrammetric reconstructions. Three-dimensional (3D) photogrammetry is being increasingly used for digital reconstruction of physical objects. There has been limited investigation on how picture quality influences 3D photogrammetric reconstructions. The purpose of this research was to experimentally compare reconstructions generated from images taken with larger versus smaller iris openings. Four cadaveric feet (2 left and 2 right) from 2 cadavers were dissected, removing skin, fascia, and muscles. Pictures were taken with the feet placed upright on a turntable. For each foot, 24 pictures were taken at f/3.2 (open iris) and f/11 (closed iris), with respective exposure time and ISO determined using a photography light meter. Bones were digitally reconstructed and the talar dome was visually compared between open versus closed iris reconstructions. Closed iris reconstructions more accurately represented the talar dome, for example having gradual and smooth curved transition between the top surface and the medial and lateral sides. Open iris reconstructions had greater distortion, such as sharp, bulging edges on these transitions. To increase the quality of 3D photogrammetric digital reconstructions, a closed iris, restricting only focused light to pass through the camera's iris to the sensor is recommended. Using a closed iris may be particularly important for bone surfaces covered with hyaline cartilage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".