Quasi-dense Correspondence in Stereo Images Using Multiple Coupled Snakes
Bibliographic record
Abstract
In this paper, we present a new method to establish quasi-dense correspondence between a pair of stereo images without camera calibration. Our proposed method is based on the traditional snake formulation using an energy function. The energy function incorporates a new matching term. When the energy function is minimized, the control points along the curves in a stereo pair are matched. Moreover, a penalty term is applied to prevent two snakes in the same image to overlap. In our method, snakes in both images are coupled and can change their shapes simultaneously. In particular, the control points on the curves are matched and evolved at the same time. Comparing our method to the conventional stereo methods, the latter requires camera parameters in order to employ the epipolar constraint to locate correspondences while ours does not. Comparing to the traditional feature-based stereo methods such as those using SIFT and SURF, the number of correspondences established by our method is significantly higher. Our method is especially suitable in scenes for which there are many textureless regions, and hence SIFT/SURF can find few matches. In order to evaluate the accuracy of different methods, the fundamental matrix is computed using the correspondences established by each method. The experimental results from both synthetic and real images are compared to the ground truth and to the conventional sparse matching method to demonstrate that our method has significant improvement over 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".