Panomorph lenses: a low-cost solution for panoramic surveillance
Bibliographic record
Abstract
Historically, the US Army, border security agencies as well as in transportation security planner has recognized the advantages of panoramic imagers, increased areas coverage with fewer cameras, tracking of multiple target simultaneously and others. However, panoramic imager has blind zone when using catadioptric system and required high bandwidth and heavy installation with fisheye lens to get an interesting resolution. The novel Panomorph lens is the heart of the new surveillance and security system developed by ImmerVision. The Panomorph lens is anticipated to be a new generation of lenses that can be used with NTSC or PAL camera to provide equivalent resolution than a 2 MPixels system but at a fraction of the cost by using existing facilities (cable, camera…). By introducing at the optical design stage a proper angular to pixel function (distortion), the new lens can provide a higher resolution in a define zone of interest than a standard fisheye. To achieve a gain in resolution, a pixel size well corrected image spot size is required. Our development included a strong optical design effort that resulted in an all refractive anamorphic panoramic imager with uncompromised image resolution for longer range detection in the zone of interest. The paper describes the development and real performance status of the Panomorph lens. Other components of the ImmerVision system include image correction, image compression and data transferred to handle devices. The same approach can also be used with IR imager where the number of pixel is limited.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".