Realtime HDR (High Dynamic Range) video for eyetap wearable computers, FPGA-based seeing aids, and glasseyes (EyeTaps)
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".