Extraction of characteristics from an image by analysis with multiple spatial resolutions
Bibliographic record
Abstract
By analogy to the mammalian visual system, a scheme is presented for image characteristic extraction from several channels of analysis with spatial frequencies. The significant contours on several scales are integrated to form a connected sketch. Frequency bands are generated by the bias of a Gaussian filter to obtain a multiresolution pyramid. A top-down analysis is adopted to generate the final sketch. After choice of the vigilance index (number of layers of the pyramid used for analysis), information from a higher level is used to check the processing at the underlying level. The vigilance index controls the nature of information to be extracted. Integration of this contour detector into a system of automatic inspection of printed circuit boards shows that a small vigilance index makes it possible to verify the state of components, their identification and connection, whereas a large index leads to a sketch suitable for recognition of the components and verification of their correct positioning on the board.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".