Stereoscopic chroma key matting using statistical analysis in CIECAM02 color space
Bibliographic record
Abstract
In a chroma keying system, the foreground objects are recorded in front of background with solid color, which is used as side information to efficiently and accurately separate the foreground from the background. Although the technique of chroma keying has been widely used for decades, there is little work on its application to stereoscopic images/videos in either industry or academia fields. We propose a new chroma key matting method to generate high quality matting results. Specifically, the colors of foreground objects to be chroma keyed are histogram analysed in CIECAM02 color space, which can provide better hue/saturation consistency compared to previous human visual system (HVS) based color spaces such as HSV, CIELab and IPT. The color of background region, which is occluded by foreground objects, is estimated by spatial affinity. With the estimated foreground/background color for each pixel, the foreground objects can be accurately extracted along with its transparency map α. By doing this, we can generate depth map with accurate and sharp boundary, which is essential for post 3D processing such as depth image based rendering (DIBR).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".