Calibration and stability analysis of medium-format digital cameras
Bibliographic record
Abstract
Recent developments in digital cameras in terms of an increase in size of the charged coupled device and the complementary metal oxide semiconductor arrays, as well as a reduction in costs, are leading to their use for traditional and new photogrammetric, surveying, and mapping functions. Such usage should be preceded by careful calibration of the implemented cameras in order to determine their interior orientation parameters. In addition, the wide diversity of expected users mandates the development of a convenient calibration procedure that does not require professional photogrammetrists and/or surveyors. This paper introduces a methodology for calibrating medium-format digital cameras using a test field consisting of straight lines and a few signalized point targets. A framework for the automatic extraction of the linear features and the point targets from the images, and for their incorporation into the calibration procedure, is presented and tested. In addition, the research introduces an approach for testing the camera stability, in which the degree of similarity between the bundles reconstructed from two sets of interior orientation parameters is quantitatively evaluated. Experimental results with real data proved the feasibility of the line-based self-calibration approach. In addition, the analysis of the internal characteristics of the utilized camera estimated from various calibration sessions revealed the camera's stability over a long period.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".