Efficient eye pointing with a fisheye lens
Bibliographic record
Abstract
This paper evaluates refinements to existing eye pointing techniques involving a fisheye lens. We use a fisheye lens and a video-based eye tracker to locally magnify the display at the point of the user’s gaze. Our gaze-contingent fisheye facilitates eye pointing and selection of magnified (expanded) targets. Two novel interaction techniques are evaluated for managing the fisheye, both dependent on real-time analysis of the user’s eye movements. Unlike previous attempts at gaze-contingent fisheye control, our key innovation is to hide the fisheye during visual search, and morph the fisheye into view as soon as the user completes a saccadic eye movement and has begun fixating a target. This style of interaction allows the user to maintain an overview of the desktop during search while selectively zooming in on the foveal region of interest during selection. Comparison of these interaction styles with ones where the fisheye is continuously slaved to the user’s gaze (omnipresent) or is not used to affect target expansion (nonexistent) shows performance benefits in terms of speed and accuracy. Key words: Eye Pointing, Fisheyes 1
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".