Real‐time video chroma keying: a parallel approach based on local texture and global colour distribution
Bibliographic record
Abstract
This study presents an automatic, human perception based chroma‐keying algorithm that extracts the objects of interest (i.e. foreground) from monochromatic background. Given an image to be chroma keyed, the global colour distribution and the local texture property are analysed in CIECAM02 colour appearance model. After the analysis, input image is automatically segmented into three parts: foreground, background, and uncertain regions. Afterwards, the background colour is propagated from known background to uncertain region by using interpolation functions; and the foreground colour is estimated based on global colour distribution and a linear cost criteria. The quantitative and perceptual comparisons on the matting results show that the proposed method can reliably remove the background region, correctly restore the intrinsic foreground colour, and accurately keep the fine details. In addition, the authors implement the proposed method on a heterogeneous parallel computing architecture which efficiently distributes the workload among different processors. The simulation results show that the foreground objects can be accurately extracted from high‐definition and/or ultra‐high‐definition videos in real time.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".