Interactive venation-based leaf shape modeling: Natural Phenomena and Special Effects
Bibliographic record
Abstract
We describe a representation for tree leaves and an interactive modeling system for creating realistic close-up images of leaf clusters. The planar outline of the leaf and the larger members of its venation system are strong factors in the recognition of plant species and as such are essential to realistic imaging. The larger veins also play a major biological role in determining the leaf surface shape and it is this role that we mimic in the shape modeling discussed in this paper. The proposed representation uses a model of a leaf consisting of a three-dimensional skeleton formed by its larger veins and a surface membrane representing the leaf lamina that spans the void between the veins. The veins play two roles. They can be interactively modified to create the 3-D shape of the leaf model. They also provide for realistic light and shadow effects when rendered as generalized cylinders using measured width parameters. The representation consists of two coupled data structures, a tree data structure of veins for the leaf skeleton and an unstructured triangular mesh for the leaf membrane. The skeleton is modified by the user of the modeling system, and the membrane mesh is a surface mesh that follows the skeleton shape computed using harmonic interpolation. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".