Adaptive reconstruction of anatomical surfaces from human body measurements
Bibliographic record
Abstract
A technique that adaptively fits a deformable Bezier surface to partial or whole human body measurements for free-form surface reconstruction is described. The proposed method utilizes an unconventional neural network, called a Bernstein basis function (BBF) network, which performs a weighted summation of rational Bernstein polynomial basis functions. Modifying the number of basis neurons is equivalent to changing the degree of the Bernstein polynomials. Each BBF network determines the control points of a low-order rational Bezier surface that best approximates the shape of randomly organized coordinate data. A key feature of the algorithm is that the measured data does not have to be re-ordered or parameterized prior to fitting. In addition, the rational Bezier surface retains the relative position of the parametric coordinates (u,v) as it deforms. Once the adaptation phase is complete, the weights of the network can be entered directly into a variety of commercially available geometric modeling and CAD/CAM packages for shape reconstruction. An experimental study is presented to demonstrate the effectiveness of the BBF network for generating rational Bezier surfaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".