Bibliographic record
Abstract
We report both the theoretical development and experimental results of a visual servoing technique which makes use of canonical decomposition of the affine motion model in computer vision, to six one-parameter groups (2D translation, dilation, rotation and 2D affine shears). Because the canonical decomposition yields one-parameter basis groups, a localized form of correlation can be used to obtain measures of these 6 image deformations. In addition, since these basis functions obey the rules of quaternion algebra, they form an integrable representation of 2D motion, and are thereby compatible with methods of recursive estimation, such as Kalman filtering. Two principles of visual servoing are featured in the system which was developed to verify this model. The first is, that by nesting the perceptual module within an active robotic system, the act of controlling the motion of the sensor adds important information, without which, the perception of certain observables would not be possible. Secondly, whether computer vision is used as a pre-processing stage before displaying information to a human operator, or whether that information will be used within an automated visual feedback loop, the following principle is common: the system is more efficient if the visual information can be transformed in a way which makes the task-specific information explicit.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".