INVESTIGATION INTO THE BEHAVIOUR AND MODELLING OF CHROMATIC ABERRATIONS IN NON-METRIC DIGITAL CAMERAS
Bibliographic record
Abstract
Abstract. Chromatic aberration in colour digital camera imagery can affect the accuracy of photogrammetric reconstruction. Both longitudinal and transverse chromatic aberrations can be effectively modelled by making separate measurements in each of the blue, green and red colour bands and performing a specialized self-calibrating bundle adjustment. This paper presents the results of an investigation with two aims. The first aim is to quantify the presence of chromatic aberration in two sets of cameras: the six individual cameras comprising a Ladybug5 system, calibrated simultaneously in air; and four GoPro Hero 5 cameras calibrated independently under water. The second aim is to investigate the impacts of imposing different constraints in the self-calibration adjustment. To this end, four different adjustment cases were performed for all ten cameras: independent adjustment of the observations from each colour band; combined adjustment of all colour bands’ observations with common object points; combined adjustment of all colour bands with common object points and common exterior orientation parameters for each colour band triplet; and combined adjustment with common object points and certain common interior orientation parameters. The results show that the Ladybug5 cameras exhibit a small (1-2 pixel) amount of transverse chromatic aberration but no longitudinal chromatic aberration. The GoPro Hero 5 cameras exhibit significant (25 pixel) transverse chromatic aberration as well as longitudinal chromatic aberration. The principal distance was essentially independent of the adjustment case for the Ladybug5, but it was not for the GoPro Hero 5. The principal point position and precision were both affected considerably by adjustment case. Radial lens distortion was invariant to the adjustment case. The impact of adjustment case on decentring distortion was minimal in both cases.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".