Optical characteristics optimized for machine perception using learning-based losses backpropagation through optical simulation pipeline
Bibliographic record
Abstract
As more and more cameras are used for machine perception, the optical design process still relies on key indicators such as point spread function (PSF), modulated transfer unction (MTF) based on aberration minimization. This process has proven efficient for human vision but is not tailored for machine perception. Given a specific computer vision task, it is not always necessary to target the same key performance indicators (KPIs) than when images are visualized by humans. Moreover, this image quality might change during a camera lifespan with the appearance of defocus for example. It is crucial to be able to determine how this kind of degradation can affect a computer vision task. In this work we study the impact of defocus on 2D object identification and show that, for a certain design, it is not impacted by image degradation under a certain threshold. We also demonstrate that this threshold is higher for lower f-number which makes them better design candidates.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".