Similarity Measures for Automatic Defect Detection on Patterned Textures
Bibliographic record
Abstract
Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".