Block/object-based algorithm for estimating true motion fields
Bibliographic record
Abstract
A hybrid algorithm for estimating true motion fields is proposed in this paper. This algorithm consists of three steps: block-based initial motion estimation, image segmentation, and wrong motion vector correction based on objects. The hierarchical block-matching algorithms are improved for the initial motion estimation. The improved algorithm uses an adaptive technique to propagate motion vectors between hierarchical levels. It produces accurate motion field everywhere, except in the areas of motion occlusion. In order to correct wrong motion vectors in the areas of motion occlusion, the current image is segmented into objects and an object-based method is proposed to process the estimated motion fields. With the object-based method, wrong motion vectors are detected by approximating the estimated motion field in each object with a motion model, and are corrected using an object-adaptive interpolator. The object-adaptive interpolator is also used to increase the density of the motion field. Experimental results show that the improved hierarchical block-matching algorithm outperforms the conventional hierarchical block- matching algorithms. The proposed algorithm results in dense motion fields that are smooth within every object, discontinuous between objects of different motion, and very close to the true motion fields.
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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) | 0.003 | 0.002 |
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".