Digital video projection for interactive entertainment
Bibliographic record
Abstract
Digital projection technology allows for effective and entertaining spatial augmented reality applications. Leveraging the capabilities of state-of-the-art motion capture or commodity depth sensors to determine the 3D position and pose of objects in real time, it is possible to project dynamic graphical content on arbitrary surfaces. In this thesis, we explore computer vision techniques including projector-camera calibration and 3D surface reconstruction to accurately map contents on a static surface, a dynamically moving rigid-body surface, and a human face. Quantitatively, the projection error is measured for both displaced and moving rigid-body objects. The accuracy of projection on a displaced object is within 2 pixels in 71% of the extent of the measured points in 320 mm by 400 mm working area. The system's response time to object movement is dictated primarily by that of the latency of the acquisition and display devices used, and a prediction filter is implemented for delay compensation. As an application of such a dynamic projection mapping system, we studied digital facial augmentation whereby participants can have the experience of "painting" on someone's face, or even on their own, by observing the projection in a mirror.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".