Semiautomatic Segmentation with Compact Shapre Prior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a semiautomatic segmentation algorithm, that can segment an object of interest from its background based on a single user selected seed. We are able to obtain reliable and robust segmentation with such low user interaction by assuming that the object to be segmented is of compact shape (we define this assumption later). We base our work on the powerful Graph Cut segmentation algorithm of Boykov and Jolly [2]. As additional benefit of incorporating the compact shape prior we are able to bias the graph cuts segmentation framework towards larger objects. It helps to counteract the well known bias of [2] to shorter segmentation boundaries. Segmentation results are quite sensitive to the choice of parameters, and so another contribution of our paper is that we show how to select the parameters automatically. We demonstrate the effectiveness of our method on the challenging industrial application of transistor gate segmentation in an integrated chip, for which it produces highly accurate results in realtime.
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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 | 0.000 | 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 it