Shape Registration Using Deformable Self-Organizing Feature Maps
Bibliographic record
Abstract
A novel approach to matching freeform surfaces for shape registration and object recognition is described in this paper. The proposed method builds a surface mesh of the underlying object geometry by iteratively deforming the nodal lattice of a spherical self-organizing feature map (SOFM) to “best” fit the measured 3D coordinate data. The final topology of the deformed mesh is, therefore, equivalent to the original lattice of the SOFM. Each node in the final mesh represents a cluster of coordinate points that lie in close spatial proximity in the input data space. In this way, closed surfaces with identical node topologies are created from different data sets. Information about node connectivity is then extracted from the ordered lattice and used to determine local surface features for correspondence matching. Based on the matched nodes, rigid body transformations between the original data sets can be determined. The shape registration algorithm enables comparisons to be made between different sized data sets or data acquired from similar freeform objects with arbitrary pose. The method is illustrated using measured coordinate data from three objects with complex freeform surface geometry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".