Calibration of an underwater stereoscopic vision system
Bibliographic record
Abstract
Although the problem of terrestrial camera calibration has been studied extensively, this knowledge does not necessarily transfer to the underwater environment directly. Because of the significant differences between the optical properties of the two transfer media, it is necessary to address the task of underwater camera calibration as a unique problem. Correspondingly, this paper studies the differences between terrestrial and underwater camera calibration and shows that the calibrated camera models are significantly different in these two environments. Thus, the necessity for in-situ calibration for an underwater environment is quantitatively ascertained. In addition, an underwater stereoscopic vision system is calibrated employing two calibration algorithms; namely, the Rahman-Krouglicof and the Heikkila algorithm. Since the mathematical formulations of the calibration problem in both environments are identical, a general calibration algorithm can be adopted for an underwater application provided that it is robust enough to overcome the suboptimal imaging conditions of an underwater environment. In order to identify a suitable calibration algorithm for aquatic environments, this paper assesses the stereoscopic performance of the two calibration algorithms in terms of reconstruction error. The experimental data confirms that the Rahman-Krouglicof algorithm is well-equipped to address the peculiarities of underwater 3D reconstruction.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".