Context-Preserving Volume Image Exploration Using A 3D Painting Metaphor
Bibliographic record
Abstract
<p>This thesis combines a 3Dinteraction model with a Maximum Intensity Difference Accumulation (MIDA) volume visualization algorithm to create a technique for exploring volumetric datasets. The interaction model is based on a 3D Painting metaphor where a user selects a Region of Interest (ROI) by “painting” a 3D envelope enclosing features of interest. The result is an exploration technique that is intuitive to use and easy to learn even for non-expert users. The painting based model and the MIDA algorithm also provide visualization flexibility by allowing for different combinations of volumetric exploration operations. In addition, the various algorithms comprising the exploration technique have been implemented to take full advantage of parallel computational capabilities of modern Graphics Processing Units (GPUs), thus providing real-time interaction and high-quality visualisation. Finally, the contributions of the thesis are validated by a series of experiments and a user study.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".