Automatic Projector Calibration Using Self-Identifying Patterns
Why this work is in the frame
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Bibliographic record
Abstract
Calibrating multiple monitor or projector display elements to provide a composite image can be a time-consuming task if performed manually. Ideally the user would like to roughly aim a number of projectors at a surface, define the desired display corners, and have some automatic method to align the display. A digital camera and computer vision can be used to calibrate the projectors with the assistance of self-identifying patterns. To account for distortion effects and to equalize brightness, it is desirable to know the mapping of many points within each projector image. A small set of images can be projected from each display element if a self-identifying pattern is used. An array of ARTag markers are used as a self-identifying pattern which is displayed in turn by each of the display monitors or projectors and recognized in the camera image. In this way an ad-hoc arrangement of projectors can be calibrated in seconds. Experimental results are shown validating this architecture.
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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 | 0.000 | 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 | 0.001 |
| 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