An energy-basfd framework using global spatial constraints for the stereo correspondence problem
Bibliographic record
Abstract
This paper investigates the use of a region-based approach for the stereo matching problem. We have stated this problem in a commonly adopted global energy-based framework. Our energy-based model mixes a local and robust regularization term with global spatial constraints. These constraints are related to a (precomputed) partition into homogeneous regions with identical disparity. In practice, our approach assigns a single disparity to regions instead of individual pixels. These regions, used to globally constrain the ill-posed nature of our minimization problem, are estimated by combining an unsupervised Markovian segmentation and a roughly estimated disparity map. This disparity map is computed with a basic winner-take-all (WTA) procedure. The proposed global energy function seems to be well suited to find good disparity discontinuities at object boundaries, especially when the number of disparities is large. An iterated conditional modes (ICM) algorithm is used to optimize this global energy function. We provide experimental results on real stereo image pairs. A quality measure, based on ground truth data, is used to evaluate the performance of our algorithm. Results indicate that our approach is fast and performs well compared to other existing methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".