Enhanced image processing and archiving capabilities of magneto-optical imaging for non-destructive evaluation
Bibliographic record
Abstract
In its current state, the wide acceptance of the Magneto-Optical Imaging (MOI) technique is hindered due to noise, lack of recordable results, and impossibility of data post-processing. This paper presents some add-ons made to a commercial MOI system to ease the image interpretation, archiving and reporting of the results. In addition, a few image processing techniques are also employed in an attempt to perform automatic flaw detection. The recording capability of the MOI instrument output images was addressed by digitizing the video signal in video or image files. To help with the identification of the damage location and distance between images, a rotary quadrature encoder was mounted onto the MOI scan head. The use of the encoder allowed the identification of the inspection location with respect to a reference position, such as the beginning of the scan. Moreover, it allowed saving images at fixed intervals, which were then stitched into a single image, thus simplifying the post inspection analysis process. Both live and post-inspection image processing capabilities were made available. Implemented image processing included background subtraction, de-noising, contrast adjustment and morphological operation, among others. Contrast stretching transform and background subtractions were found to be among the most powerful techniques that could be used in simplifying the image interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".