Photon Detection and Color Perception at Low Light Levels
Bibliographic record
Abstract
Working under low light conditions is of particular interest in machine vision applications such as night vision, tone-mapping techniques, low-light imaging, photography, and surveillance cameras. This work aims at investigating the perception of color at low light situations imposed by physical principles governing photon emission. The impact of the probabilistic nature of photon emission on our color perception becomes more significant at low light levels. In this regard, physical principles are leveraged to develop a framework to take into account the effects of low light level on color vision. Results of this study shows that the normalized spectral power distribution of light changes with light intensity and becomes more uncertain at low light situation as a result of which the uncertainty of color perception increases. Furthermore, a color patch at low light levels give rise to uncertain color measurements whose chromaticities form an elliptic shape inside the chromaticity diagram around the high intensity chromaticity of the color patch. The size of these ellipses is a function of the light intensity and the chromaticity of color patches however the orientation of the ellipses depends only on the patch chromaticity and not on the light level. Moreover, the results of this work indicate that the spectral composition of light is a determining factor in the size and orientation of the ellipses. The elliptic shape of measured samples is a result of the Poisson distribution governing photon emission together with the form of human cone spectral sensitivity functions and can partly explain the elliptic shape of MacAdam ellipses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".