A Novel Solution for Registration of Stereo Digital Torso Images of Scoliosis Patients
Bibliographic record
Abstract
This paper presents a procedure for registration of a pair of stereo digital images giving an improvement in accuracy and speed over existing methods. It does so by a novel approach combining color based image segmentation and differential geometry. It involves three stages: image segmentation, adaptive local pixel matching, and deferential geometry in a tree weighted belief propagation procedure. The registration was compared to 2 existing registration procedures, segment-based adaptive belief propagation (adaptive BP) and color-weighted hierarchical belief propagation (hierarchical BP). A 3D scan of a mannequin was obtained and errors in reconstruction were measured for each of the 360 cross sections of the mannequin. The proposed procedure outperforms existing methods, particularly for high curvature regions and significantly large cross sections. Its accuracy of reconstruction ranged from 85-100% compared to 75-100% for other existing methods. It was 35% to 40% faster. This work provides a solution to the registration problem and is an important step in developing a cost effective technique for measuring torso shape and symmetry of scoliosis patients using stereo digital cameras.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".