Multi-Projector Content Preservation with Linear Filters
Bibliographic record
Abstract
Using aligned overlapping image projectors provides several ad-vantages when compared to a single projector: increased bright-ness, additional redundancy, and increased pixel density withina region of the screen. Aligning content between projectors isachieved by applying space transformation operations to the de-sired output. The transformation operations often degrade the qual-ity of the original image due to sampling and quantization. Thetransformation applied for a given projector is typically done in iso-lation of all other content-projector transformations. However, it ispossible to warp the images with prior knowledge of each othersuch that they utilize the increase in effective pixel density. Thisallows for an increase in the perceptual quality of the resultingstacked content. This paper presents a novel method of increas-ing the perceptual quality within multi-projector configurations. Amachine learning approach is used to train a linear filtering basedmodel that conditions the individual projected images on each other
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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 | 0.002 |
| 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".