Three-dimensional optical metrology and models for non-contact diffuse optical tomography of small animals
Bibliographic record
Abstract
We introduce a novel approach for calibrating an axis of rotation in a 3D optical metrology system. The system uses a stereo camera pair, along with rotation and translation stages for obtaining a 3D model of the surface of small animals. The metrology system will be part of a fully non-contact diffuse optical tomography (DOT) scanner for small animal imaging. The rotation axis calibration technique is based on measuring, with the stereo pair, the 3D position of a small ball as it is moved by the rotation stage (turntable). Our system has the advantage of using the tomograph's laser beam to measure the outer shape of the subject, thereby reducing overall system complexity, and allowing simultaneous surface and DOT measurements. Additionnaly, the exact position where laser light penetrates the animal is measured, while traditionally, this information is indirectly inferred with less accuracy. This information plays an important role in a tomographic reconstruction algorithm. Our new approach for the calibration of the rotation axis is compared to another technique we previously developed, where a checkerboard pattern is tracked instead of a ball. We present measurements of a reference shape and a small animal taken by our system.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".