Fabric Texture Analysis Using Computer Vision Techniques
Bibliographic record
Abstract
This paper presents inexpensive computer vision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automatic recognition of weave pattern and the accurate measurement of yarn counts by analyzing fabric sample images. We propose a surface roughness indicator FDFFT, which is the 3-D surface fractal dimension measurement calculated from the 2-D fast Fourier transform of high-resolution 3-D surface scan. The proposed weave pattern recognition method was validated by using computer-simulated woven samples and real woven fabric images. All weave patterns of the tested fabric samples were successfully recognized, and computed yarn counts were consistent with the manual counts. The rotation invariance and scale invariance of FDFFTwere validated with fractal Brownian images. Moreover, to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FDFFTis a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".