Adaptive thresholding for detection of nonsignificant vectors in noisy image sequences
Bibliographic record
Abstract
In noisy image sequences, block matching motion estimation generates erroneous motion vectors since the algorithm tries to correlate noise. We present an adaptive threshold test to detect blocks for which only nonsignificant motion vectors can be estimated. Vectors of these blocks are then assigned the zero vector before any block motion estimation is performed. By nonsignificant, we refer to motion vectors of nonmoving areas as well as vectors of moving areas for which the noise level is too high to allow a good estimation of the motion. The detection of these vectors reduces the computational complexity of the BMA and the entropy of the motion field. The algorithm is embedded in a hierarchical BMA and takes advantage of their different spectral characteristics to discriminate between the frame difference energy due to noise and due to motion. The algorithm is also efficient for low noise sequences where it can be used to initialize a segmentation of moving objects from the background.
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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".