VFCCV snake: A novel active contour model combining edge and regional information
Bibliographic record
Abstract
Active contour models have been widely used for image segmentation. Among leading models of active contour is vector-field convolution (VFC), a parametric active contour that improves the popular gradient vector flow (GVF) model. However VFC is still sensitive to noise and can be easily trapped in cluttered regions of an image because it only considers edge information. Based on the geometric active contour model proposed by Chan and Vese, this paper introduces a novel active contour model that incorporates region information in VFC in order to take advantage of edge and regional information. This new model, which we refer to as VFCCV snake, is implemented in the parametric active contour framework, and has control on topology especially in noisy images and images with boundary gaps. Experimental results on both synthetic and real images show superior performance of our VFCCV snake to state-of-the-art leading active contour methods.
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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 | 0.002 |
| 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".