ASSESSMENT OF CHROMATIC ABERRATIONS FOR GOPRO 3 CAMERAS IN UNDERWATER ENVIRONMENTS
Bibliographic record
Abstract
Abstract. With underwater photogrammetric mapping becoming more prominent due to the lower costs for waterproof cameras as well as lower costs for underwater platforms, the aim of this research is to investigate chromatic aberration in underwater environments. Chromatic aberration in in-air applications is to be known to systematically influence the observations of up to a few pixels. In order to achieve pixel-level positioning accuracy, this systematic influence needs further investigation. However, while chromatic aberration studies have been performed for in-air environments, there is a lack of research to quantify the influence of chromatic aberration in underwater environments. Using images captured in a water tank from three different GoPro cameras in five datasets, we investigate possible chromatic aberration by running two different adjustments on the extracted red (R), green (G) and blue (B) bands. The first adjustment is an adjustment that calculates the interior orientation parameters for each set of images independently in a free network adjustment. The second adjustment solves for all interior orientation parameters (for R, G, and B channels) in a combined adjustment per camera, constraining the point observations in object space. We were able to quantify significant chromatic aberrations in our evaluation, with the largest aberrations observed for red band followed by green and blue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".