Background estimation using graph cuts and inpainting
Bibliographic record
Abstract
In this paper, we propose a new method, which requires no interactive operation, to estimate background from an image sequence with occluding objects. The images are taken from the same viewpoint under similar illumination conditions. Our method combines the information from input images by selecting the appropriate pixels to construct the background. We have two simple assumptions for the input image sequence: each background pixel has to be disclosed at least once and some parts of the background are never occluded. We propose a cost function that includes a data term and a smoothness term. A unique feature of our data term is that it has not only the stationary term, but also a new predicted term obtained using an image inpainting technique. The smoothness term guarantees that the output is visually smooth so that there is no need for post-processing. The cost is minimized by applying graph cuts optimization. We apply our algorithm to several complex natural scenes as well as to an image sequence with different camera exposure settings, and the results are encouraging.
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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".