Image coding technique for 3-D back reconstruction.
Bibliographic record
Abstract
This work investigated the use of coded-line patterns to improve image correspondence and 3D reconstruction. The objective was to define an optimum structured light pattern to facilitate the construction of topographic maps of the trunks of scoliosis patients. The system consisted of stereo CCD television cameras, a slide projector to create the coded-line pattern and a computer with video acquisition card. The optimum pattern in terms of generating correct results with minimum computing time was a line pattern consisting of 6 groupings of 3-level grey lines. This chosen pattern was investigated using known 3D objects to determine the effectiveness, resolution and computational time to correlate stereo images. The selected structured light pattern consisted of a repeated pattern of white (W), grey (G) and black (B) lines. Each line was 4mm wide on the back surface. The pattern consisted of 6 groups of 3 lines, WBG WGB GWB GBW BGW BWG. Using known 3D models of a section of a cylinder, a plate with steps and a ramp-like object; the coded line system was tested under typical clinical lighting conditions. For the 3D test objects, errors in correspondence occurred in 2-4 % of stereo pixels. The processing time varied from 10-12 minutes. The 3D resolution obtained was 4mm.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".