Fuzzy integral based region merging for watershed image segmentation
Bibliographic record
Abstract
A fuzzy integral based region merging algorithm is presented to deal with the issue of oversegmentation due to the watershed transform. The algorithms integrates region and edge features together using a fuzzy logic based fusion method. Firstly, preprocessing and watershed segmentation are performed on the image. Depending on the complexity of the image content the segmentation process may produce many more regions than what are really expected to exist in the image. To reduce the number of regions that results due to oversegmentation, a fusion process is applied to these regions recursively according to the principle of the maximum fuzzy integral. After transferring these features to memberships that reflect the degree that a given region to belong to its neighboring regions, an integration (fusion) scheme is used to compute a fuzzy integral based on which a decision is made with respect to the region merging. To evaluate the performance of the proposed approach it has been applied to real images such as MRI and natural images.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".