IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION
Bibliographic record
Abstract
Abstract. This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".