Reciprocal-wedge transform in motion stereo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it