VDESIGN: Toward image segmentation and composition in cave using finger interactions
Bibliographic record
Abstract
The Cave Automatic Virtual Environment (CAVE) system is a fully immersive virtual reality system, which can provide users with a realistic experience and a large freedom of interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using finger interactions. Specifically, we focus on the function of image segmentation and composition in the vDesign system. In vDesign, the user wears a marker on each hand. The interactions of the user are triggered based on the real-time positions of the markers. We design multiple finger interactions for image segmentation and image composition. In image segmentation, the user can use the right finger to select the interested object and the left finger to select the unrelated background. Based on the user's selection, a graph-cut based image segmentation is employed to extract the interested object from the image. In image composition, the user can move, rotate, and scale the segmented objects with fingers and combine them together into a final image. We implemented the vDesign prototype and conducted experiments to compare the finger interactions and the traditional wand interactions. The experimental results demonstrated that the proposed finger interactions can provide faster and more accurate interactions compared to the traditional wand interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".