Image Inpainting and Segmentation using Hierarchical Level Set Method
Bibliographic record
Abstract
Image inpainting is an artistic procedure to recover a damaged painting or picture. In this paper, we propose a novel approach for image inpainting. In this approach, the Mumford-Shah (MS) model and the level set method are employed to estimate image structure of the damaged region. This approach has been successfully used in image segmentation problem. Compared to some other inpainting methods, the MS model approach can detect and preserve edges in the inpainting areas. We propose in this paper a fast and efficient algorithm which can achieve both inpainting and segmentation. In previous works on the MS model, only one or two level set functions are used to segment an image. While this approach works well on some simple images, detailed edges cannot be detected on complicated images. Although multi-level set functions can be used to segment an image into many regions, the traditional approach causes extensive computations and the solutions depend on the location of the initial curves. Our proposed approach utilizes faster hierarchical level set method and can guarantee convergence independent of initial conditions. Because we can detect both the main structure and the detailed edges, the approach can preserve detailed edges in the inpainting area. Experimental results demonstrate the advantage of our method.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".