Half-Occluded Regions and Detection of Pseudoscopy
Bibliographic record
Abstract
We propose that left- and right-half-occlusion regions contain information that can distinguish between stereoscopic and pseudoscopic display conditions. A machine vision method is presented based on this idea, which detects pseudo copy using only the histograms of left and right half-occlusion pixel locations. Two psychophysical experiments are described which study the ability of human viewers to detect, or be influenced by, pseudoscopic display. The results of this study show that, during free viewing of HD 3D video imagery, humans judged pseudoscopic imagery to be of lower quality than stereoscopic imagery. Subjects performed at a 72% rate in deciding whether a short 5-second video clip was presented stereoscopically or pseudoscopically. Subjects were observed to fixate on half-occlusion regions with a frequency of 15.8%, as opposed to a frequency of 7.8% indicated by random chance. Viewers more frequently (17.4%) fixated on half-occlusion regions when making correct decisions then when they were incorrect (11.9%).
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".