Dedicated testing setup for panoramic lenses
Bibliographic record
Abstract
Panoramic imaging is of growing importance in many applications around the world spurred by the development of digital imaging. Panoramic lens characteristics are unique and their careful characterization can be a challenge. For example, the price to pay for a large field of view in this type of lens is high distortion in the image. For vision applications like security or inspection, a precise knowledge of the distortion introduced by panoramic lenses is essential to produce natural unwrapped views to the operator. Of special concern is the image quality which must be uniformed over the entire field of view because all directions are equally important. In addition, two hemispheric images can also be stitched together to create a complete spherical image. For these reasons, we have developed a dedicated setup to study the distortion and the image quality produced by panoramic lenses. The test setup is made of a 75-cm radius cylindrical structure with targets placed on it. Using referenced equally-spaced targets, we obtained the radial image mapping curves for various azymuthal angles, allowing us to calculate the full-field resolution map. Also, transition targets were used to find field-dependent spatial frequency where the MTF is 50%. We tested four different panoramic lenses, two panomorph lenses and two fisheyes. For each lens, we discussed the experimental resolution and MTF curves and compared some of those results to theoretical design data.
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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 | 0.002 |
| 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 | 0.002 | 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".