Distributed imaging using compound eye sensors
Bibliographic record
Abstract
The capture of a wide field of view (FOV) scene by dividing it into multiple sub-images is a technique with many precedents in the natural world, the most familiar being the compound eyes of insects and arthropods. Artificial structures of networked cameras and simple compound eyes have been constructed for applications in robotics and machine vision. Previous work in this laboratory has explored the construction and calibration of sensors which produce multiple small images (of ~150 pixels in diameter) for high-speed object tracking. In this paper design options are presented for electronic compound eyes consisting of 101 - 103 identical 'eyelets'. To implement a compound eye, multiple sub-images can be captured by distributing cameras and/or image collection optics. Figures of merit for comparisons will be developed to illustrate the impact of design choices on the field of view, resolution, information rate, image processing, calibration, environmental sensitivity and compatibility with integrated CMOS imagers. Whereas compound eyes in nature are outward-looking, the methodology and subsystems for an outward-looking compound-eye sensor are similar for in an inward-looking sensor, although inward-looking sensors have a common region viewable to all eyelets simultaneously. The paper addresses the design considerations for compound eyes in both outward-looking and inward-looking configurations.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".