Real-Time and Accurate Pupil Detection Based Retro-Oriented Mind and Ellipse Trend Analysis
Bibliographic record
Abstract
This study focuses on designing a pupil ellipse detector for wearable eye trackers.The detector uses both a traditional method producing pupil patches in different resolutions and a learning model segmenting these patches.Therefore, the frequency is increased as the input size of the learning model will be reduced according to the structure of the received image.This novel approach in the pupil detection field was named as Retro-Oriented Mind (ROM).The study also presents metrics measuring the segmentation accuracy and a correction mechanism improving ellipse parameters if metric scores are not acceptable.The combination of novel metrics and correction mechanisms was named as Pupil Ellipse Trend Analysis (PETA).Using ROM and PETA, the proposed study has achieved an accuracy of over 90% and a frequency of more than 120 Hz (from about 30 Hz) in analyses of LPW and Dikablis datasets.These measurements reveal the potential of the study to be used for both medical and general purposes.Code and details: https://github.com/Serif-NNR/rom-peta-
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".