Defects segmentation for wood floor based on image fusion method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The surface defects of wood floor directly influence its quality and sorting levels. To solve the problem of slow speed and low accuracy of defects segmentation methods,a fast visual sorting system was designed and a novel segmenting method based on image fusion was proposed. R component image was extracted first and scaling methods were applied to the image. Defects were rapidly located through region growing algorithms in low-dimensional space. Then gradient interpolation method was used to restore the image,and defects were marked to generate the reference image. The wavelet transform was used to identify the margin of the reference image. Finally,dual-threshold growth criterions and taboo table of rapidly located defects were set up to complete the taboo search from the margin of rapidly located region to the outside. The result of the experiment made on 20 sample images with sound knots,dead knots and cracks revealed that the average segmentation time of this method is 13. 21ms,and the accuracy of defect segmentation is 96. 8%.
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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 | 0.000 |
| 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