Neural network stereo image segmentation for directed coordinate measuring machine part programming
Bibliographic record
Abstract
Reverse engineering is used in industry to create a geometric model from an existing physical part. Often, this task is initiated by the collection of 3-D data using a contact sensor, such as a touch probe, mounted onto the end effector of a coordinate measuring machine (CMM). A CCD camera, mounted along with the touch probe, is utilized to automatically direct the digitization process. Stereo images taken with the CCD camera are used to both segment the object into its component surface patches and to locate the object on the CMM bed. A Kohonen self-organizing network is used to segment the stereo images. Areas of constant grey level intensity are used as seed locations from which patches are grown in the network. The stronger patches (those in areas of constant grey level intensity) compete and eventually dominate neighbouring patches in areas of less grey level consistency. In this winner take all strategy, the number of surface patches need not be known beforehand, and learning phases are unnecessary. This is ideal for reverse engineering, as the object shape and complexity is often unknown. Surface patch location is achieved by matching segmented patches between the stereo image pairs. Experiments on a planar test object demonstrate this system's robustness. By combining a CCD camera with the CMM touch probe, an automated 3-D digitization system is developed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".