Computer vision signal processing on graphics processing units
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
This paper shows speedups attained by using computer graphics hardware for implementation of computer vision algorithms by efficiently mapping mathematical operations of computer vision onto modem computer graphics architecture. As an example computer vision algorithm, we implement a real-time projective camera motion tracking routine on modern, GeForce FX class GPUs (graphics processing units). Algorithms are implemented using OpenGL and the nVIDIA Cg fragment shaders. Trade-offs between computer vision requirements and GPU resources are discussed. Algorithm implementation is examined closely, and hardware bottlenecks are addressed to examine the performance of GPU architecture for computer vision. It is shown that significant speedups can be achieved, while leaving the CPU free for other signal processing tasks. Applications of our work include wearable, computer mediated reality systems that use both computer vision and computer graphics, and require realtime processing with low-latency and high throughput provided by modem GPUs.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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