Multi-dimensional sub-sample motion estimation: Initial results
Bibliographic record
Abstract
Motion estimation in ultrasound images lies at the heart of many modern signal processing applications. Therefore, its computational cost, accuracy, and precision are of significant importance. In this work, we consider the problem of 3D subsample motion estimation. Extending our previous 2D work, we define a 3D polynomial interpolation function that fits the discrete pattern matching coefficients obtained from digitized 3D echo data. A joint estimation of the axial, lateral and elevational motion is obtained by finding the extremum of this 3D polynomial fit. We study this method using a synthetic phantom and the Field II ultrasound simulation software. The mean absolute axial, lateral, and elevational biases of the proposed 3D polynomial fitting were found to be 0.0066, 0.0075, and 0.0047 of a sample (corresponding to 127 nm, 2.25 ¿m, and 710 nm), respectively, for an axial sampling of 40 MHz (¿ 19.3 ¿m), a lateral sampling of 300 ¿m, and an elevational sampling of 150 ¿m. The mean axial, lateral, and elevational standard deviations of the proposed 3D polynomial fitting were found to be 0.0130, 0.0290, and 0.0569 of a sample (corresponding to 250 nm, 8.71 ¿m, and 8.54 ¿m), respectively. Experimental results demonstrating the viability of the proposed method are also presented.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".