A variable template matching algorithm for anomaly detection versus kernel density and gradient convolution algorithms and ResNet-50
Bibliographic record
Abstract
This study first proposes a variable template matching algorithm for anomaly detection. Variational forms of template for defect with multiple scales, rotations and perspective transformations are included to improve its variational robustness. The normalized cross correlation between the template and the sliding window on the image is computed as the matching result. Secondly, it proposes a kernel density algorithm, in which a lower kernel density index (intensity percentile/range) of the sliding window indicates a potential anomaly. Lastly, it proposes a gradient convolution algorithm. These three traditional computer vision algorithms are implemented for anomaly detection of a group of biological images, and results are compared with that of the convolutional neural network ResNet-50. Results show that the variable template matching algorithm achieves superior performance (true positive 82% and false positive 4.9%) than both the kernel density and gradient convolution algorithms. Its detection rate is lower than the prediction of ResNet-50 (true positive 86%), but it is much faster and does not need any train images as an unsupervised learning. Therefore, it can be a potential candidate for object detection of small dataset or for a quick solution.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".