Reverse Design and Application of Irregular Planar Part Based on Image Processing
Bibliographic record
Abstract
By using a computer vision detection system to obtain high resolution images of a machine part, a kind of reverse design method of solid modeling of irregular planar part with aided implementation of computer vision was proposed in this paper, which integrates image processing function of Matlab software with solid modeling function of computer aided design (CAD) software. The method used a calibrated digital camera to get the image of the tested part, a three-dimensional entity vector model may be built up after image inversion, edge detection, vectorization process of binary image and size matching were operated sequentially. The results of image reverse design showed that it is an easy and convenient way to reverse irregular planar parts based on image processing. One of its remarkable advantages is the saving of design period and the reduction of design cost. Its measurement error can be controlled within 0.1 mm, and can meet general precision requirement of application occasions. Reversed parts may provide a model basis for further analysis on mechanism assembling and motion simulation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".