A Joint Approach to Global Motion Estimation and Motion Segmentation From a Coarsely Sampled Motion Vector Field
Bibliographic record
Abstract
In many content-based video processing systems, the presence of moving objects limits the accuracy of global motion estimation (GME). On the other hand, the inaccuracy of global motion parameter estimates affects the performance of motion segmentation. In this paper, we introduce a procedure for simultaneous object segmentation and GME from a coarsely sampled (i.e., block-based) motion vector (MV) field. The procedure starts with removing MV outliers from the MV field, and then performs GME to obtain an estimate of global motion parameters. Using these estimates, global motion is removed from the MV field, and moving region segmentation is performed on this compensated MV field. MVs in the moving regions are treated as outliers in the context of GME in the next round of processing. Iterating between GME and motion segmentation helps improve both GME and segmentation accuracy. Experimental results demonstrate the advantage of the proposed approach over state-of-the-art methods on both synthetic motion fields and MVs from real video sequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".