<title>Object-based postprocessing of block motion fields for video applications</title>
Bibliographic record
Abstract
It is likely that in many applications block-matching techniques for motion estimation will be further used. In this paper, a novel object-based approach for enhancement of motion fields generated by block matching is proposed. Herein, a block matching is first applied in parallel with a fast spatial image segmentation. Then, a rule-based object postprocessing strategy is used where each object is partitioned into sub-objects and each sub-object motion histogram first separately analyzed. The sub-object treatment is, in particular, useful when image segmentation errors occur. Then, using plausibility histogram tests, object motions are segregated into translational or non-translational motion. For non-translational motion, a single motion-vector per sub-object is first assigned. Then motion vectors of the sub-objects are examined according to plausibility criteria and adjusted in order to create smooth motion inside the whole object. As a result, blocking artifacts are reduced and a more accurate estimation is achieved. Another interesting result is that motion vectors are implicitly assigned to pixels of covered/exposed areas. In the paper, performance comparison of the new approach and block matching methods is given. Furthermore, a fast unsupervised image segmentation method of reduced complexity aimed at separating objects is proposed. This method is based on a binarization method and morphological edge detection. The binarization combines local and global texture-homogeneity tests based on special homogeneity masks which implicitly take possible edges into account for object separation. The paper contributes also a novel formulation of binary morphological erosion, dilation and binary edge detection. The presented segmentation uses few parameters which are automatically adjusted to the amount of noise in the image and to the local standard deviation.
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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 | 0.001 | 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) | 0.068 | 0.035 |
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".