Design and automatic calibration of a head mounted operating binocular for augmented reality applications in computer-aided surgery
Bibliographic record
Abstract
In the last years we developed and tested a head mounted display (HMD) for augmented reality applications in computer aided surgery. This HMD was developed by adapting the Varioscope AF3 (Life Optics, Vienna), an operating binocular with variable zoom and focus. One of the drawbacks of the AF3 was the missing possibility to set the zoom and focus values automatically via a machine usable interface, necessary for automatic calibration of the device. The paper presents the successor of the Varioscope AF3, the Varioscope M5 adapted for augmented reality by our lab. This device has an interface for machine controlled setting of the zoom and focus lens groups via RS 232. This enabled us to develop an automated calibration using a calibration grid mounted on a linear positioner. The position of the grid was controlled using a stepping motor controller connected via IEEE 488. The calibration grid was equipped with automatically detectable fiducial points using varying cross values of consecutive points. The resulting point pairs were used for a camera calibration with Tsai's algorithm. Tracker probes (Traxtal, Toronto) were mounted on the HMD and onto the calibration grid to derive the transformation from the coordinate system of the HMD into the system of the displays. The error of this calibrations was measured comparing the position of the tip of a bayonet probe calculated by the algorithm and found in the image of a camera mounted at the eyepiece of the device. Averaging 16 positions of the probe this deviation was found to be 0.97 ± 0.22 mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".