An optimized two-step camera calibration method
Why this work is in the frame
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Bibliographic record
Abstract
An optimized two-step camera calibration algorithm is developed. The proposed method starts with the well known linear calibration which approximates the transformation as a 3/spl times/4 matrix. Based on the results of the linear calibration and the camera model we construct the 4/spl times/4 homogeneous transformation matrix. Quaternion algebra is used to extract the optimum rotation matrix and this optimization is later extended to the other calibration parameters. Our calibration method includes nonlinear optimization which takes into consideration lens distortions. The convergence rate of the nonlinear optimization is accelerated by three more objective functions we introduced. To assess the accuracy of our proposed quaternion method, Euclidean norm of the error matrix between the original and computed homogeneous transformation matrices is calculated and compared to those of the existing methods. Simulations show that the quaternion method yields more accurate results both before and after the nonlinear optimization.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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