Mediated reality using computer graphics hardware for computer vision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it