Fabric Texture Analysis Using Computer Vision Techniques
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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 FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFT</sub> , 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 FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFT</sub> were validated with fractal Brownian images. Moreover, to evaluate the correctness of FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFT</sub> , we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFT</sub> is a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.
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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