Evaluation of Digital Image Discretization Error in Droplet Shape Measurement Using Simulation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Droplet shape measurement using image based techniques can be conducted using shape parameters which consist of a number of geometric features of a droplet image. The accuracy of calculating these shape parameters and their capability in revealing shape deviation is considerably affected by the discretization of the image with a camera CCD. In this paper, a simulation of digital images is conducted to investigate the error caused by image discretization. The effect of this error on calculating area, perimeter, and also a selected number of shape parameters are investigated. Digital images of circular and elliptical discs at different image/pixel size ratio and locations relative to the pixel grid have been generated to simulate the projected view. Results show that the shape parameters demonstrate different levels of sensitivity to the desirable factor of shape deviation and the undesirable factor of image discretization. A “clearance factor” has been suggested and used to rank the shape parameters based on their compensation between sensitivity to shape deviation and image discretization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it