Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video
Bibliographic record
Abstract
In this paper, we propose a novel method for the automatic segmentation of a foreground layer from a natural scene in real time by fusing infrared, color and edge information. This method improves on previous video foreground/background segmentation algorithms by making the system totally independent of changes in ambient lighting. A powerful data acquisition unit was developed using an optical technique that automatically gives synchronized and registered color video and infrared (IR) video at 850 nm. Using the fused information produced by the IR video one can then automatically initialize a pentamap, which is processed by the graph cuts algorithm. We show that the pentamap can simplify the graph construction process, improve the efficiency of the graph cuts algorithm, and allow to use a more reliable color Gaussian mixture model (GMM) than the usual trimap method. At the end of the processing pipeline, a simple border-blurring algorithm is used to simulate matting effects on the foreground-background boundary. Experimental results are presented.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".