A Perceptual Depth Shape-based CRF Model for Deformable Surface Labeling
Bibliographic record
Abstract
Real-time deformable scene understanding is a challenging task. In this paper, we address this problem by using Conditional random fields (CRFs) framework and perceptual shape salience occupancy patterns. CRF is a powerful probabilistic model that has been widely used for labelling image segments. It is particularly well-suited to modelling local interactions and global consistency among bottom-up regions (e.g. super pixels). However, its capacity could be limited if the underlying feature potentials are not well reflecting the scene properties. We propose a depth shape-based CRF model for deformable surface (sand in our case) labelling by utilizing expressive novel shape salience occupancy patterns (SOP). Experimental results demonstrate the effectiveness and robustness of the method on recorded video datasets. While our work has concentrated on sand surface labelling, the approach can be applied to other surface materials (e.g. snow, mud), and extended to non-planar surfaces as well (e.g. sculpting blocks).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".