A novel 3D torso image reconstruction procedure using a pair of digital stereo back images
Bibliographic record
Abstract
This paper presents a novel procedure for creating a 3D torso image from a pair of stereo digital 2D back images.The aim of this procedure is to obtain 3D images that can be used for assessment of external spinal deformities in scoliosis.Scoliosis is a condition characterized by lateral deviation of the spine coupled with rotation of individual vertebra resulting in visible torso asymmetries.The procedure provides clinicians with a cost effective and mobile setup of acquiring 3D images.To improve the registration process, a novel approach combining tree weighted colour based image segmentation and differential geometry was developed.Image reconstruction involved pre-processing, triangulation and texture application to obtain a 3D image.Analysis was performed using human subjects and objects of known dimension.Evaluation of system performance was done against existing stereovision procedures and range scanning systems.The final 3D image was compared to that obtained from the Konica Minolta Vivid 700 laser scanner.Each image was divided into 360 cross sections for evaluation against size and shape.The 3D image reconstructed from this novel procedure was 75-100% accurate when compared against the 3D image from the laser scanner.The results demonstrate that the procedure is a cost effective clinical tool for assessing torso shape and symmetry.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".