Mediated reality using computer graphics hardware for computer vision
Bibliographic record
Abstract
Wearable, camera based, head-tracking systems use spatial image registration algorithms to align images taken as the wearer gazes around their environment. This allows for computer-generated information to appear to the user as though it was anchored in the real world. Often, these algorithms require creation of a multiscale Gaussian pyramid or repetitive re-projection of the images. Such operations, however can be computationally expensive, and such head-tracking algorithms are desired to run in real-time on a body borne computer In this paper we present a method of using the 3D computer graphics hardware that is available in a typical wearable computer to accelerate the repetitive image projections required in many computer vision algorithms. We apply this "graphics for vision" technique to a wearable camera based head-tracking algorithm, implemented on a wearable computer with 3D graphics hardware. We perform an analysis of the acceleration achieved by applying graphics hardware to computer vision to create a Mediated Reality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".