Multi-scale gesture recognition from time-varying contours
Bibliographic record
Abstract
A novel method is introduced to recognize and estimate the scale of time-varying human gestures. It exploits the changes in contours along spatiotemporal directions. Each contour is first parameterized as a 2D function of radius vs. cumulative contour length, and a 3D surface is composed from a sequence of such functions. In a two-phase recognition process, dynamic time warping is employed to rule out significantly different gesture models, and then mutual information (MI) is applied for matching the remaining models. The system has been tested on 8 gestures performed by 5 subjects with varied time scales. The two-phase process is compared against exhaustively testing three similarity measures based upon MI, correlation, and nonparametric kernel density estimation. Experimental results demonstrate that the exhaustive application of MI is the most robust with a recognition rate of 90.6%, however, the two-phase approach is much more computationally efficient with a comparable recognition rate of 90.0%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".