Use of Random Dot Patterns in Achieving X-Ray Vision for Near-Field Applications of Stereoscopic Video-Based Augmented Reality Displays
Bibliographic record
Abstract
This article addresses some of the challenges involved with creating a stereoscopic video augmented reality “X-ray vision” display for near-field applications, which enables presentation of computer-generated objects as if they lie behind a real object surface, while maintaining the ability to effectively perceive information that might be present on that surface. To achieve this, we propose a method in which patterns consisting of randomly distributed dots are overlaid onto the real surface prior to the rendering of a virtual object behind the real surface using stereoscopic disparity. It was hypothesized that, even though the virtual object is occluding the real object’s surface, the addition of the random dot patterns should increase the strength of the binocular disparity cue, resulting in improved performance in localizing the virtual object behind the surface. In Phase I of the experiment reported here, the feasibility of the display principle was confirmed, and concurrently the effects of relative dot size and dot density on the presence and sensitivity of any perceptual bias in localizing the virtual object within the vicinity of a flat, real surface with a periodic texture were assessed. In Phase II, the effect of relative dot size and dot density on perceiving the impression of transparency of the same real surface while preserving detection of surface information was investigated. Results revealed an advantage of the proposed method in comparison with the “No Pattern” condition for the transparency ratings. Surface information preservation was also shown to decrease with increasing dot density and relative dot size.
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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 | 0.002 |
| 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) | 0.001 | 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".