Integrating multiple views with virtual mirrors to facilitate scene understanding
Bibliographic record
Abstract
In this article, an image integration technique called Virtual Mirroring (VM) is evaluated. VM is a technique that combines multiple 2D views of a 3D scene into a single composite image by overlaying views onto virtual mirrors. Given multiple views of a scene, one view is augmented with the remaining views by placing virtual mirrors on the first view and overlaying onto them the corresponding remaining views. Unlike a standard array presentation, where 2D views are not integrated and simply placed adjacent to one another, the VM presentation preserves the relative location, orientation, and scale between views. As such, it is our contention that humans will fare better at performing certain visual tasks, such as scene identification, when viewing a 3D scene via a VM presentation than when viewing an array presentation. We performed an experiment on 12 participants, where participants were required to identify 96 scenes both with a VM and an array presentation and we compared their % correctness and response times. Moreover, we studied the effects of adding an auditory attentional load on performance. We found that regardless of load, participants were able to identify scenes using VM presentation with greater accuracy and at greater speeds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".