Automatic calibration of low-cost digital cameras
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
Recent developments of digital cameras in terms of the size of charge-coupled device (CCD) arrays and reduced costs are leading to their applications in traditional as well as new photogrammetric, surveying, and mapping functions. Digital cameras, intended to replace conventional film-based mapping cameras, are becoming available along with many smaller formats capable of precise measurement applications. All such cameras require careful calibration to determine their metric characteristics, which are essential to carrying out photogrammetric activities. We introduce a new approach for incorporating straight lines in a bundle adjustment for calibrating off-the-shelf, low-cost digital cameras. The optimal configuration for successfully deriving the distortion parameters is considered when establishing the required test field. Moreover, a framework for automatic extraction of the straight lines in the images is presented and tested. The developed calibration procedure can be used as an efficient tool to investigate the most appropriate model that compensates for various distortions associated with the camera being calibrated. Experiments performed to compare line-based with traditional point-based self-calibration methods prove the feasibility of the suggested approach.
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.
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 | 0.000 | 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