Texture analysis of images using principal component analysis
Bibliographic record
Abstract
Extracting texture/roughness information from grayscale or multispectral images for off-line quality control, or on-line feedback control is a difficult problem. Several statistical, structural & spectral texture analysis approaches for grayscale images (using various pre-defined filters etc.) have been suggested in the literature1, 2 In this paper we propose a new approach based on Multivariate Image Analysis techniques using multi-way Principal Component Analysis. Prior to analysis the grayscale images are transformed into three-dimensional pixel intensity arrays through spatial shifting of the image in several directions followed by stacking the shifted images on top of each other. The resulting three -dimensional image data is a multivariate image where the third (i.e. variable) dimension is the spatial shifting index. Multi-way PCA is then used to extract features (PC scores), which contain the greatest amount of variation. Plots of the observed values of these scores against one another define a score space. Certain regions of this score space contain the texture information of the grayscale image. By masking these regions and tracking the number of pixels having features that fall in these regions, or by comparing the score spaces with template exemplars, one is able to monitor changes in the image surface textural properties. The approach is illustrated using a set of grayscale images of the surface of steel sheet. Based on the textural features extracted from the surface images a simple classification scheme is devised in which each sample image is assigned into one of two classes representing good or bad surface characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".