Toward application of image tracking in laparoscopic surgery
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
In the past few years, the application of image tracking in laparoscopic surgery has gained popularity. In such an approach, the robot controls the movement of the laparoscope (endoscope) in surgery by following the feedback of the image tracking information. The calibration of the distorted endoscopic images is an important first step toward realizing such an approach. However, there exist very few methods for such calibration. In the paper, a new method of calibration and measurement of the endoscopic images is proposed. The high distortion of the endoscopic images makes the calibration difficult. Endoscopic images have typical barrel distortion. That is, the distortion increases as the observation becomes more and more eccentric. We propose an idea that only concentrates on the center area of the image. Within this area, we can ignore the complex and time-consuming distortion-correction step. A simple method for finding the intrinsic calibration parameters is then proposed. Design of a marker for the tracking task is also described. Some experimental results are presented to show the feasibility of the proposed method.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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