Non-Contact 3D Coordinates Measurement of Cross-Cutting Feature Points on the Surface of Large-Scale Workpiece Based on Machine Vision Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it