A GMM-MRF Based Image Segmentation approach for Interface Level Estimation
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Bibliographic record
Abstract
Maintaining the desired interface level between the top froth layer and the liquid layer plays an important role in achieving high recovery of products in oil sands and related process industries. As varying throughputs and downstream disturbances tend to change the interface level over time, it is an important indicator of the process behavior. In this paper, we propose an approach based on Gaussian mixture model and Markov Random Field (MRF) based unsupervised image segmentation to achieve the real-time accurate measurement of the interface. The image processing problem is solved as a Maximum a Posteriori (MAP) estimation problem employing the MRF framework and the parameters are estimated using the EM algorithm. The proposed approach is validated using the images captured from a laboratory scale equipment designed to simulate the industrial PSV interface.
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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) | 0.002 | 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