Comparison of performance of some common Hartmann-Shack centroid estimation methods
Bibliographic record
Abstract
The accuracy of the estimation of optical aberrations by measuring the distorted wave front using a Hartmann-Shack wave front sensor (HSWS) is mainly dependent upon the measurement accuracy of the centroid of the focal spot. The most commonly used methods for centroid estimation such as the brightest spot centroid; first moment centroid; weighted center of gravity and intensity weighted center of gravity, are generally applied on the entire individual sub-apertures of the lens let array. However, these processes of centroid estimation are sensitive to the influence of reflections, scattered light, and noise; especially in the case where the signal spot area is smaller compared to the whole sub-aperture area. In this paper, we give a comparison of performance of the commonly used centroiding methods on estimation of optical aberrations, with and without the use of some pre-processing steps (thresholding, Gaussian smoothing and adaptive windowing). As an example we use the aberrations of the human eye model. This is done using the raw data collected from a custom made ophthalmic aberrometer and a model eye to emulate myopic and hyper-metropic defocus values up to 2 Diopters. We show that the use of any simple centroiding algorithm is sufficient in the case of ophthalmic applications for estimating aberrations within the typical clinically acceptable limits of a quarter Diopter margins, when certain pre-processing steps to reduce the impact of external factors are used.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".