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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".