Sequential Image Restoration and Segmentation for Interface Detection in Primary Separation Cells
Bibliographic record
Abstract
The primary separation cell (PSC) plays a key role in bitumen recovery during oil sands extraction. Controlling the froth-middling interface level in PSC is vital for optimal bitumen recovery. Existing sensors for the interface level measurement can be either costly or less reliable. This paper introduces an image restoration algorithm to enhance degraded images of PSC sight glasses followed by an image segmentation technique, which can serve as an alternative for interface level estimation. The restoration algorithm uses a regular state-space model with a skew-t distribution for measurement noise to account for image contamination. States and parameters are estimated using an expectation-maximization (EM) algorithm along with a robust Kalman filter (KF). The restored images are segmented using a Gaussian mixture model (GMM) with Markov random fields (MRF) for interface detection. Experimental results on a lab-scale PSC demonstrate the method’s effectiveness in improving interface level estimation compared to the existing models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".