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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".