<title>Recognition of free-form shapes using spherical SOFMs</title>
Bibliographic record
Abstract
Recognition of free-form objects is a difficult task in a variety of engineering applications such as reverse engineering and product inspection. Most recognition systems can handle polyhedral objects that are defined by a set of primitives such as vertices, edges, or planar faces. However, free-form shapes have curved surfaces and often lack identifiable markers such as corners or sharp discontinuities. This paper presents a novel approach to creating structured representations of free-form surfaces that can be used for object recognition. The proposed method maps the three-dimensional coordinate data acquired by a range sensor onto a spherical self-organizing feature map (SOFM). The adaptation algorithm of the SOFm develops a topological order to the measured coordinate data such that connected nodes on the spherical map represent neighboring points on the object surface. Features are then extracted at each node of the SOFM. The feature vector is computed using a simple function that relates the node's positional vector to each of its neighboring nodes, within a circular are of one unit radius, in the SOFM. The feature vectors are used to establish a correspondence between the spherical map generated by an unknown free-form shape and maps of all the reference models. Any two free-form shapes can be matched for recognition purposes by registering the spherical SOFMs and determining the minimum registration error. This approach enables the unknown object to be in an arbitrary orientation. An experimental study is presented in order to demonstrate the effectiveness of this approach. The spatial coordinate data of a human foot and a toy in the shape of a pelican are used for illustrative purposes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.112 |
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".