Bibliographic record
Abstract
The reciprocal-wedge transform (RWT) facilitates space-variant sensing which enables effective use of variable-resolution data and the reduction of total amount of the sensory data. This paper presents two motion stereo methods that exploit the important properties of the RWT, i.e., the anisotropic variable resolution and the preservation of linear features. It is shown that the RWT is suitable for the correspondence process in both lateral and longitudinal motion stereo which deal with disparities of corresponding features along epipolar lines. Multiple frames of motion stereo images are employed to improve precision and error rate of the depth recovery. In the lateral motion stereo the RWT is applied in both space and time domains to transform the x-t epipolar plane in ordinary motion stereo images into a new /spl omega/-/spl tau/ epipolar plane. In the longitudinal motion stereo, the reciprocity of the RWT restores the nonlinearity in the original x-t epipolar plane. Consequently, in both cases, the correspondence problem in variable-resolution motion stereo is reduced to a simpler problem of extracting collinear points in the epipolar plane. A voting algorithm for accumulating multiple evidence is developed. The proposed method is potentially applicable to active sensing for automated inspection on assembly lines, autonomous road vehicle navigation, airport runway surveillance, etc. Preliminary experimental results are demonstrated.>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".