An Efficient and Fast Active Contour Model for Salient Object Detection
Bibliographic record
Abstract
In this paper, we investigate the polarity information to improve the active contour model proposed by Chunming et al. Unlike the traditional level set formulations, the variational level set formulation proposed by forces the level set function to be close to a signed distance function,and therefore completely eliminates the need of the reinitialization procedure and speeds up the curve evolution.However, like the majority of classical active contour models,the model proposed by relies on a gradient based stopping function, depending on the image gradient, to stop the curve evolution. Consequently, using gradient information for noisy and textured images, the evolving curve may pass through or stop far from the salient object boundaries.Moreover, in this case, the isotropic smoothing Gaussian has to be strong, which will smooth the edges too. For these reasons, we propose the use of a polarity based stopping function. In fact, comparatively to the gradient information,the polarity information accurately distinguishes the boundaries or edges of the salient objects. Hence, combining the polarity information with the active contour model of we obtain a fast and efficient active contour model for salient object detection. Experiments are performed on several images to show the advantage of the polarity based active contour.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".