The question of accuracy with geometric camera calibration
Bibliographic record
Abstract
In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters which describe the image formation process for a given analytical model of the machine vision system. An accurate, reliable calibration procedure is essential for most industrial machine vision applications including mechanical metrology, robot assembly, reverse engineering, stereo vision etc. One of the most systematic calibration procedures for 3D machine vision applications was proposed by Heikkila in which a comprehensive set of camera parameters is automatically evaluated by observing a calibration target consisting of two perpendicular planes, each with 256 circular control points. Other similar techniques employ a checkerboard pattern as a target and use the vertices of the squares as control points. While these techniques are sound from a theoretical point of view, they do not adequately speak to the question of measurement accuracy. The objective of this work is to gain and understanding of the problems associated with Geometric Camera Calibration through the application of Design of Experiments. A response surface methodology, namely a CCD Design, is carried to analyze the effects. This paper also highlights the issue of calibration accuracy by addressing the following fundamental question: Assuming a certain tolerance or uncertainty in the calibration target, what is the expected error with respect to the measured camera parameters and what is the impact on the final 3D machine vision application?
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.
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 | 0.041 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".