Pseudo‐color enhancement and its segmentation for femtosecond laser spot image
Bibliographic record
Abstract
Abstract When using femtosecond laser processing silicon wafer, arises laser spot along with the plasma diffraction. Comparatively studied the spot images of silicon wafer which was in three processing movement states as follows: towards the left, stop, towards the right, found that the three dimensional Gauss mean ablation energy of spot image almost kept the same, this provides experimental support for femtosecond laser feedback processing based on Gauss energy of spot image. Then the following image enhancement strategies are proposed: pseudo color transformation for spot image, color decomposition in RGB space and image superposition of G component, and the quality of the spot image is improved. In addition, adopted the method of Particle Swarm Optimization (PSO) or K‐means respectively, analyzed the segmentation effect for spot image: through traversal compares the gray value of image pixel and fitness function, realized the spot image segmentation with PSO, and the clustering and segmentation for data cluster of image pixel was realized by K‐means. Finally, overcome the shortcomings of PSO and K‐means, the ideal segmentation for spot target image is realized by combining the two methods.
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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 | 0.000 | 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) | 0.001 | 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".