Hierarchical Segmentation of Range Images with Contour Constraints
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a new algorithm to segment in continuous parametric regions range images. The algorithm starts with an initial partition of small first order regions using a robust fitting algorithm constrained by the detection of depth and orientation discontinuities. The algorithm then optimally group these regions into larger and larger regions using parametric functions until an approximation limit is reached. The algorithm uses Bayesian decision theory to determine the local optimal grouping and the complexity of the parametric model used to represent the range signal. After the segmentation process an exact description of the boundary of each region is computed from the mutual intersections of the extracted surfaces. Experimental results show significant improvement of region boundary localization. A systematic comparison of our algorithm to the most well known algorithm in the literature is presented to highlight the contributions of this paper.
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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