Non-Contact 3D Coordinates Measurement of Cross-Cutting Feature Points on the Surface of Large-Scale Workpiece Based on Machine Vision Method
Bibliographic record
Abstract
3D coordinates measurement of feature points on the surface of large-scale workpiece is important and difficult, various relative measuring methods have been presented in recent years, and machine vision method has been paid more attentions by researchers. The application of machine vision method in the 3D coordinate measurement of feature points on the surface of large-scale workpiece is discussed in this paper, and an accurate, simple, new measuring method is proposed. The design of the measuring system mainly consider the following aspects: the principle and composition of the measuring system; the study on the monocular vision for the camera locating; the calibration method of CCD camera; image processing of cross-cutting feature point and the calculation of its 2D image coordinates; the study of binocular stereo vision based on the large-scale CMM. The experimental results indicate the correctness and reliability of the new measuring method and show that it can be used in the noncontact 3D coordinates measurement of cross-cutting feature points on the surface of large-scale workpiece
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".