Robust snake convergence based on dynamic programming
Bibliographic record
Abstract
The extraction of contours using deformable models, such as snakes, is a problem of great interest in computer vision, particular in areas of medical imaging and tracking. Snakes have been widely studied and many methods are available. In most cases, the snake converges towards the optimal contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. This approach is elegant, but frequently mis-converges in the presence of noise or complex contours. To address these limitations, a novel discrete snake is proposed which treats the two energy terms separately. Essentially, the proposed method is a deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a Hidden Markov Model (HMM) and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted. The proposed implementation is straightforward and achieves dramatic speed and accuracy improvement compared to other 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 | 0.000 |
| Open science | 0.001 | 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".