On Sensor Bias in Experimental Methods for Comparing Interest-Point, Saliency, and Recognition Algorithms
Bibliographic record
Abstract
Most current algorithm evaluation protocols use large image databases, but give little consideration to imaging characteristics used to create the data sets. This paper evaluates the effects of camera shutter speed and voltage gain under simultaneous changes in illumination and demonstrates significant differences in the sensitivities of popular vision algorithms under variable illumination, shutter speed, and gain. These results show that offline data sets used to evaluate vision algorithms typically suffer from a significant sensor specific bias which can make many of the experimental methodologies used to evaluate vision algorithms unable to provide results that generalize in less controlled environments. We show that for typical indoor scenes, the different saturation levels of the color filters are easily reached, leading to the occurrence of localized saturation which is not exclusively based on the scene radiance but on the spectral density of individual colors present in the scene. Even under constant illumination, foreshortening effects due to surface orientation can affect feature detection and saliency. Finally, we demonstrate that active and purposive control of the shutter speed and gain can lead to significantly more reliable feature detection under varying illumination and nonconstant viewpoints.
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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 | 0.041 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".