A Novel System for Ocular Surface Temperature Measurement and Tracking
Bibliographic record
Abstract
Ocular surface temperature (OST) is affected by changes in eye physiology caused by normal homeostasis, environmental changes, or systemic and local disease. OST can help a physician diagnose eye disease with improved accuracy and provide useful information for eye research. This paper presents a novel system, including novel hardware design and novel algorithms, capable of automatically measuring and tracking OST from the cornea over any period of time. The system uses an infrared (IR) camera and a visible (VIS) camera to capture synchronous thermal and visible videos, respectively, from the eye surface. The frames for each camera video sequence are then registered together (video registration) using two sets of control points. The points are manually selected on the first pair of timestamped IR/VIS frames and tracked over all subsequent frames using the Lucas–Kanade (LK) optical flow algorithm (point tracking). A mean square error (MSE) of 5.43±2.01 pixels was reported for salient point tracking of the IR video and 6.81±2.32 pixels for tracking of the VIS video. Overall MSE for registration was 5.03 ±1.82 pixels. The corneal area was segmented in the VIS images and localized on the IR images using the semantic segmentation method (corneal segmentation). A mean Intersection over Union (IoU) of 94.6% was found, representing the accuracy of corneal segmentation. A system for measuring and tracking eye surface temperature over time was developed. The system is able to localize the cornea on both VIS and IR images, and report temperature profiles of the cornea over the period of measurement. Experimental results show that the system can work as a tool for measuring and tracking OST over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".