Real-time simulation of visual defects with gaze-contingent display
Bibliographic record
Abstract
Effective management and treatment of glaucoma and other visual diseases depend on early diagnosis. However, early symptoms of glaucoma often go unnoticed until a significant portion of the visual field is lost. The ability to simulate the visual consequences of the disease offers potential benefits for patients and clinical education as well as for public awareness of its signs and symptoms. Experiments using simulated visual field defects could identify changes in behaviour, for example during driving, that one uses to compensate at the early stages of the disease's development. Furthermore, by understanding how visual field defects affect performance of visual tasks, we can help develop new strategies to cope with other devastating diseases such as macular degeneration. A Gaze-Contingent Display (GCD) system was developed to simulate an arbitrary visual field in a virtual environment. The system can estimate real-time gaze direction and eye position in earth-fixed coordinates during relatively large head movement, and thus it can be used in immersive projection based VE systems like the CAVE™. Arbitrary visual fields are simulated via OpenGL and Shading Language capabilities and techniques that are supported by the GPU, thus enabling fast performance in real time. In order to simulate realistic visual defects, the system performs multiple image processing operations including change in acuity, brightness, color, glare and image distortion. The final component of the system simulates different virtual scenes that the participant can navigate through and explore. As a result, this system creates an experimental environment to study the effects of low vision on everyday tasks such as driving and navigation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".