A rapid segmentation and occlusion completion method for packed granular particles considering uncertainty
Bibliographic record
Abstract
Occlusions of granular particles in images significantly affect the accuracy of evaluating particle morphology for granular materials. In this study, a novel framework of SOLO-PCNet is proposed, which can automatically segment all the particles and predict the complete contours of the occluded particles in densely packed materials. Firstly, the instance segmentation model SOLOv2 is trained for the prediction of all the detectable particles. Then a self-supervised learning algorithm PCNet-M is introduced for the inference of the complete contours of the occluded particles so that the prediction of SOLOv2 can be directly input to PCNet-M for the subsequent completion. Thereafter, the particle morphology characteristics including elongation, equivalent mean size, convexity, and circularity are automatically calculated. Then, the evaluation metrics of the segmentation model and morphology characteristics are validated, and the results exhibit the strong generalization ability of the segmentation and completion tasks. Finally, the uncertainty of the completed contours with morphology properties is explored for reliable analysis. This study successfully acquires the complete contours for each particle and provides the foundation for evaluating the mechanical properties of the packed granular materials from individual particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".