An Efficient Camera Calibration Technique Offering Robustness and Accuracy Over a Wide Range of Lens Distortion
Why this work is in the frame
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Bibliographic record
Abstract
In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters that describe the image formation process for a given analytical model of the machine vision system. Researchers working with low-cost digital cameras and off-the-shelf lenses generally favor camera calibration techniques that do not rely on specialized optical equipment, modifications to the hardware, or an a priori knowledge of the vision system. Most of the commonly used calibration techniques are based on the observation of a single 3-D target or multiple planar (2-D) targets with a large number of control points. This paper presents a novel calibration technique that offers improved accuracy, robustness, and efficiency over a wide range of lens distortion. This technique operates by minimizing the error between the reconstructed image points and their experimentally determined counterparts in "distortion free" space. This facilitates the incorporation of the exact lens distortion model. In addition, expressing spatial orientation in terms of unit quaternions greatly enhances the proposed calibration solution by formulating a minimally redundant system of equations that is free of singularities. Extensive performance benchmarking consisting of both computer simulation and experiments confirmed higher accuracy in calibration regardless of the amount of lens distortion present in the optics of the camera. This paper also experimentally confirmed that a comprehensive lens distortion model including higher order radial and tangential distortion terms improves calibration accuracy.
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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 | 0.002 |
| 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