Motion stream analysis based on perceptual feature partitioning and grouping
Bibliographic record
Abstract
We present a perceptual organization based on method for motion stream analysis. The computation model was developed based upon a perception principle: visual feature partitioning and grouping. In the method, perceptual edge features are extracted and classified into generic edge tokens (GETs) using edge tracking and partitioning on the fly. GETs are perceptually distinctive features of lines and curve segments. Various structures and patterns of GETs can be grouped in terms of the rules of perceptual organization laws. GETs are descriptive and therefore can be manipulated qualitatively. For each consecutive image pair, motion GETs (MGETs) are segmented by directly subtracting the GETs extracted in the first image from the same locations in the second image, in that no explicit GET pair matching is needed. The MGETs are then grouped into clusters based on selected rules and domain knowledge of the objects. The motion clusters are evaluated using the measure of motion persistence (over multi-frames) for eliminating unstable data, i.e. noises. Two result demonstrations include road mark following and vehicle tracking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".