YUV luma/chroma quantization and sparse correspondence for real time video stereo matching
Bibliographic record
Abstract
To realize the real time dense disparity map running at a video rate of 30 fps, the dynamic time warp algorithm (DTW) provides a robust method of stereo matching. This method requires to calculate the pixel-by-pixel similarity matrix that indicates the similarity from one pixel of the left image to a pixel of the right image in the raster profile. The size of the similarity matrix S is N2 for the raster size N. Down sampling to reduce N defeats the purpose of disparity measurement because the spatial resolution is also reduced. A method to introduce sparse samples is proposed in this paper, so that the proposed method reduces N but not sacrificing the spatial resolution of stereo matching. The denoised raster waveform is coarsely quantized to produce a binary train at each of the quantization level. The run-length for the contiguous one's is used as a feature to represent the raster waveform profile. The size of the sparse set is about N = 30 as opposed to the raster size N = 352 for the CIF size image. This idea is applied to the luma Y-image and the chroma UV-image in the YUV color space to take advantage of the concept of image segmentation by color in addition to the stereo matching normally performed in the gray scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".