Colour Perception on Optical-See-Through Displays for Ubiquitous Visualization Applications
Bibliographic record
Abstract
Optical-see-through (OST) displays afford users the ability to view information in augmented reality anywhere and anytime. However, the colours of the rendered content on OST displays, often used to encode information, may conflict with the current environment the user is situated in, leading to usability and perceptual challenges. In this Ph.D. thesis, I propose investigating how human perception of colours rendered on OST displays varies based on visual components of the user’s environment (colour, complexity, and lighting) to inform the design of ubiquitous visualization applications. Specifically, my work will study the effects of colour blending and contrast effects on interpretation performance, and model colour just-noticeable differences through psychophysical experiments. The resulting models will be used to design an automatic colour-adaptation approach for OST displays which preserves colour encodings and achieves adequate interpretability across dynamic viewing conditions. Taken together, my work will inform on perceptual challenges, and on the design of ubiquitous visualizations and colour encodings on OST displays.
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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 | 0.001 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".