Implementation of an affine-invariant feature detector in field-programmable gate arrays
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
Feature detectors are algorithms that can locate and describe points or regions of 'interest'---or features---in an image. As the complexity of the feature detection algorithm increases, so does the amount of time and computer resources required to perform it. For this reason, feature detectors are increasingly being ported to hardware circuits such as field-programmable gate arrays (FPGAs), where the inherent parallelism of these algorithms can be exploited to provide significant increase in speed. This work describes an FPGA-based implementation of the Harris-Affine feature detector introduced by Mikolajczyk and Schmid [36, 37]. The system is implemented on the Tra,nsmogrifier-4, a prototyping platform developed at the University of Toronto that includes four Altera Stratix S80 FPGAs and NTSC/VGA video interfaces. The system achieves a speed of 90--9000 times the speed of an equivalent software implementation, allowing it to process standard video (640 × 480 pixels) at 30 frames per second.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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