ImmerVol: An immersive volume visualization system
Bibliographic record
Abstract
Volume visualization is a popular technique for analyzing 3D datasets, especially in the medical domain. An immersive visual environment provides easier navigation through the rendered dataset. However, visualization is only one part of the problem. Finding an appropriate Transfer Function (TF) for mapping color and opacity values in Direct Volume Rendering (DVR) is difficult. This paper combines the benefits of the CAVE Automatic Virtual Environment with a novel approach towards TF generation for DVR, where the traditional low-level color and opacity parameter manipulations are eliminated. The TF generation process is hidden behind a Spherical Self Organizing Map (SSOM). The user interacts with the visual form of the SSOM lattice on a mobile device while viewing the corresponding rendering of the volume dataset in real time in the CAVE. The SSOM lattice is obtained through high-dimensional features extracted from the volume dataset. The color and opacity values of the TF are automatically generated based on the user's perception. Hence, the resulting TF can expose complex structures in the dataset within seconds, which the user can analyze easily and efficiently through complete immersion.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".