Stereovision-based close-up dimensional inspection
Bibliographic record
Abstract
Sheet metal strain analysis is an important tool to ensure products are manufactured within necessary tolerances. A common technique involves electrochemically etching a dark grid pattern of known size onto the flat sheet metal surface and then deforming the sheet. The change in the grid pattern after deformation can be used to calculate surface strain. The computer vision problem is to accurately detect the intersections of the grid pattern. To investigate this problem, a stereo camera system was designed and attached to a bridge style coordinate measurement machine. The stereo head consists of two high resolution monochrome CCD cameras mounted on a Renishaw PH10 motorized probe head that can be articulated into numerous, repeatable, preset positions. Stereo head calibration was achieved using Zhang’s technique with a planar target. Each probe position was calibrated using a global point set registration method to link coordinate systems. A novel approach to segmenting the grid pattern into squares involving region merging and watersheds is described. Grid intersections are determined to sub pixel accuracy and matched between images using a correlation based scheme. The accuracy of the system and experimental results are provided.
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.
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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".