Application of visual tracking for robot‐assisted 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
Abstract With the increasing popularity of laparoscopic surgery, the demand for better modes of laparoscopic surgery also increases. The current laparoscopic surgery mode requires an assistant to hold and manipulate the endoscope through commands from the surgeon. However, during lengthy surgery procedures, accurate and on‐time adjustment of the camera cannot be guaranteed due to the fatigue and hand trembling of the camera assistant. This article proposes a practical visual tracking method to achieve automated instrument localization and endoscope maneuvering in robot‐assisted laparoscopic surgery. Solutions concerning this approach, such as, endoscope calibration, marker design, distortion correction, and endoscope manipulator design are described in detail. Experimental results are presented to show the feasibility of the proposed method. © 2002 Wiley Periodicals, Inc.
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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