Novel hemispheric image formation: concepts and applications
Bibliographic record
Abstract
Panoramic and hemispheric lens technologies represent new and exciting opportunities in both imaging and projection systems. Such lenses offer intriguing applications for the transportation/automotive industry, in the protection of civilian and military areas, business. In this paper we describe a new optical design technique that provides a greater degree of freedom in producing a variety of hemispheric spatial light distribution areas. This innovative optical design strategy, of generating and controlling image mapping, has been successful in producing high-resolution imaging and projection systems. This success has subsequently generated increased interest in the high-resolution camera/projector and the concept of absolute measurement with high-resolution wide-angle lenses. The new technique described in this paper uses optimization techniques to improve the performance of a customized wide-angle lens optical system for a specific application. By adding a custom angle-to-pixel ratio at the optical design stage, this customized optical system provides ideal image coverage while reducing and optimizing signal processing. This novel image formation technique requires the development of new algorithms in order to view the panoramic image on a display without any residual distortion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".