Implementation of an affine-invariant feature detector in field-programmable gate arrays
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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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".