Bad pixel location algorithm for cell phone cameras
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As CMOS imaging technology advances, sensor to sensor differences increase, creating an increasing need for individual, per sensor, calibration. Traditionally, the cell-phone market has a low tolerance for complex per unit calibration. This paper proposes an algorithm that eliminates the need for a complex test environment and does not require a manufacturing based calibration on a per phone basis. The algorithm locates "bad pixels", pixels with light response characteristics out of the mean range of the values specified by the manufacturer in terms of light response. It uses several images captured from a sensor without using a mechanical shutter or predefined scenes. The implementation that follows uses two blocks: a dynamic detection block (local area based) and a static correction block (location table based). The dynamic block fills the location table of the static block using clustering techniques. The result of the algorithm is a list of coordinates containing the location of the found 'bad pixels'. An example is given of how this method can be applied to several different cell-phone CMOS sensors.
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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 | 0.001 | 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 | 0.000 |
| 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 it