Color range determination and alpha matting for color images
Bibliographic record
Abstract
In this paper, a novel matting method is proposed to automatically detect and separate foreground, background and transitional (unknown) regions in a color image. In order to detect the background color, K-means clustering in YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color space is firstly used to classify the background colors into a limited number of clusters. Then the spatial information is further used to refine the background and minimize the unknown regions. In this case, an image can be automatically segmented into three hard regions: foreground, background and unknown regions. For transitional (unknown) regions, the alpha matting based on Wang's robust matting algorithm is utilized to refine the accuracy of the separation results. By combining an automatical background determination metric and Wang's robust matting, the proposed matting method can handle images with single-colored or gridded background. The required user input is significantly simplified compared to conventional alpha matting schemes which require users to provide a hard image segmentation manually. The experimental results show that improved matting results can be achieved for complex unknown regions which contain semi-transparent materials or tiny objects such as hair stripes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".