Bibliographic record
Abstract
Optical flow can be used to compute motion detection, time to collision, structure, focus of expansion as well as object segmentation. Unfortunately, most optical flow techniques do not provide accurate and dense measures that are useful for these types of computations. In addition, most techniques are also slow computationally. Albeit, one method proposed by Camus is able to perform optical flow computations in real-time capitalizing on redundancies in the computation and spatial-temporal sampling trade-offs. It is a simple technique based on simulating various motions and computing the SD (sum-difference) of patches. Its problem is that the produced field is not accurate and arbitrary in aperture and blank wall situations. We show that the simulating of various futures can be used as the factored samples that produce the likelihood probabilities that can be used in a particle filtering framework. Maximization/minimization or computing the expectations of the likelihood at a particular location does not necessarily produce the proper flow. We suggest that likelihoods are well behaved when their variance is small and these can be propagated firstly to address aperture problems and secondly to address the extended blank wall problem. We show this propagation with thresholded likelihood values and speculate on how the likelihood distributions can be integrated into an algorithm that has its basis in particle filtering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".