Image Thresholding Based on Spatial Variation Attribute Similarity
Bibliographic record
Abstract
According to a recent study, image thresholding can be categorized into six groups of methods that are based on histogram shape, clustering, entropy, attribute, spatial, and local information. In this paper, we describe two algorithms for image binarization that are based on attribute similarity relying on spatial measures. The rationale of the method is to binarize an image in such a way that it best reproduces the spatial variation of the original image across several scales. Two different measures that characterize image spatial variation have been selected to pursue that objective: semivariance and lacunarity. Semivariance measures the spatial variation of a variable at a given scale. Lacunarity is a measure of translational invariance, at a given scale, and is often refer to as a measure of 'gappiness'. In both approaches, the threshold is selected so that the scale-dependant measure in the bi-level image best approximate, in the least square sense, the ones of the original image. Both methods are illustrated with remote sensing images of high spatial resolution. The results are compared with some other popular thresholding techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".