Performance evaluation of panoramic electro-optic imagers using the TOD method
Bibliographic record
Abstract
The triangle orientation discrimination (TOD) method is an emerging technique for the evaluation of electro-optical (EO) systems. In this method, the test pattern is a non-periodic equilateral triangle in one of four different orientations (apex up, down, left, or right), and the measurement procedure is a robust four-alternative forced-choice psychophysical process. This leads to a time-consuming task. Consequently, software models have been developed to replace the required human observers. These models base their decision on the orientation of the target using correlation between observed data and the set of four differently oriented targets. This study investigates for the first time how this method can be applied to highly distorted OE systems like hemispheric imagers. These types of systems have inherent large distortion, but the distortion should not be considered as an aberration but rather the result of the projection of a hemispheric field (3D) on a 2D sensor. The distortion deforms the image of the targets and image processing is usually performed to remove distortion and straighten the field of view. We present a comparison in accuracy and computational burden for the evaluation of EO system performance between cases where tested images are pre-processed and correlated to unchanged triangle targets and where untouched (distorted) images are correlated with position-wise distorted targets. This is a first evaluation of the application of the TOD with the goal of obtaining an image quality criterion for panoramic imagers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".