Chroma-Keying Based on Global Weighted Sampling and Laplacian Propagation
Bibliographic record
Abstract
Chroma-keying is an important technique for image/video background replacement, which is heavily used in film production, video game industry and news casting. In chroma-keying system, the foreground objects are shot in front of solid color background. In conventional chroma-keying systems, difference and clustering based algorithms are mainly used to separate foreground from background. However, regions cannot be reliably segmented if transparency or blurring exists, and if foreground and background region are not completely separable in color space. This paper proposes a new method to automatically remove the background color and to accurately extract the foreground objects along with their transparency properties, especially for tiny objects and large transparent area. First, a threshold based method in both HSV color space and spatial gradient space is used to roughly segment the foreground/background regions. The background color is then propagated from known background region to unknown region. Finally, the foreground color and the transparency factor are estimated based on weighted global sampling. The experimental results show that the foreground objects can be accurately extracted even if large transparent regions exist.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".