Real-time video matting using multichannel poisson equations
Bibliographic record
Abstract
A This paper presennts a novel matti ing algorithm for processing video sequences in reall-time and onlin ne. The algorithm is based on a set of novel Poissson equations that are derivved for handlinng multichannel coolor vectors, as well as the depth informatioon captured. A simmple yet effectiv ve approach is also proposed to compute an inittial alpha matte in the color space. Real-timme processing speed is achieved thro ough optimizing the algorithm for parallel processinng on the GPUs. To process live video sequences online and autonomously, a mod dified backgrounnd cut algorithm is immplemented to separate foreground and backgroound, the result of which guides the automatic trimap generation. Quantitative evaluation on stiill images show ws that the alphaa mattes extracted using the presented algorithm is much more accurate than the onnes obtained using the global Poisson matting algorithm and are comparable to that of other state-of-the-art offline image mattinng techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".