Realtime HDR (High Dynamic Range) video for eyetap wearable computers, FPGA-based seeing aids, and glasseyes (EyeTaps)
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
Realtime video HDR (High Dynamic Range) is presented in the context of a seeing aid designed originally for task-specific use (e.g. electric arc welding). It can also be built into regular eyeglasses to help people see better in everyday life. Our prototype consists of an EyeTap (electric glasses) welding helmet, with a wearable computer upon which are implemented a set of image processing algorithms that implement realtime HDR (High Dynamic Range) image processing together with applications such as mediated reality, augmediatedTM, and augmented reality. The HDR video system runs in realtime and processes 120 frames per second, in groups of three frames or four frames (e.g. a set of four differently exposed images captured every thirtieth of a second). The processing method, for implementation on FPGAs (Field Programmable Gate Arrays), achieves a realtime performance for creating HDR video using our novel compositing methods, and runs on a miniature self-contained battery-operated head-worn circuit board, without the need for a host computer. The result is an essentially self-contained miniaturizable hardware HDR camera system that could be built into smaller eyeglass frames, for use in various wearable computing and mediated/ aug-mediated reality applications, as well as to help people see better in their everyday lives.
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 | 0.001 | 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 | 0.002 |
| 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