Automatic woven fabric structure identification by using principal component analysis and fuzzy clustering
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 goal of the study is to develop automatic fabric analysis system by using inexpensive image processing techniques. In this study, we proposed a novel automatic method for woven fabric structure identification. This method is based on widely used digital image analysis techniques. It allows automatic weft yarn and warp yarn cross area segmentation through a spatial domain integral projection approach. Secondly, texture features based on grey level occurrence matrix are studied and optimized by applying principal component analysis. The optimized texture features are analyzed by fuzzy c-means clustering for classifying the different cross area states. The texture orientation features are calculated to determine the exact state of cross area. Finally, woven fabric structures, for example, weave patterns and yarn counts are automatically determined. To verify the validity of this method, a number of sample images are used. The samples have different weave types, different fiber appearances and yarn counts. The recognition results match the actual structure of tested samples.
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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 | 0.000 | 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 | 0.000 |
| 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