Dense stereo disparity map for video by sub-pixel dynamic time warp algorithm
Bibliographic record
Abstract
Dense stereo disparity map is an image indicative of the distances to the objects in the view field, constructed from the binocular stereo cameras, which can be used as robotic vision sensor etc. In order to determine the disparity, pixel correspondence between the right and left images needs to be found. A simple camera pose to keep the lens axes of two cameras parallel makes the stereo matching problem to be 1D constrained in the same raster scan line. The dynamic time warp (DTW) algorithm of the dynamic programming method efficiently solves the problem of stereo matching to match the right and left raster profiles to the accuracy of one pixel distance. In order to improve the resolution of distance, the paper proposes a method of subpixel disparity estimation that uses the cross-correlation between two local image profiles derived from the phase-only cross spectrum. The subpixel disparity is measured from the peak shifting of the spline function that interpolates discrete samples of the phase-only cross-correlation. The method complements the stereo matching by the DTW and gives fractional corrections to the disparities found by the DTW. The paper demonstrates the implementation of the method to generate a dense disparity map which can resolve one tenth of a pixel distance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".