Automatic Fabric Inspection using GLCM-based Jensen-Shannon Divergence
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Bibliographic record
Abstract
Jensen-Shannon divergence is one of the powerful information-theoretic measures that can capture mutual information between two probability distributions. In this paper, a machine vision algorithm is proposed for automatic inspection on dot patterned fabric using Jensen-Shannon divergence based on gray level co-occurrence matrix (GLCM). Input defective images are split into several periodic blocks and the gray levels are quantized from 0-255 to 0-63 to keep the GLCM compact and to reduce the computation time. Symmetric Jensen-Shannon divergence metrics are calculated from the GLCMs of each periodic block with respect to itself and all other periodic blocks to get a dissimilarity matrix. This dissimilarity matrix is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple and knot show the effectiveness of the proposed method for fabric inspection.
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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 | 0.001 |
| 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