Evaluation of image corner detectors for hardware implementation
Bibliographic record
Abstract
We analyze a number of corner detection algorithms and identify the advantages and disadvantages of each algorithm to evaluate their suitability for hardware implementation. We implemented three popular corner detectors, Plessy, Wang-Brady, and SUSAN, in software and compared them on the basis of their stability, accuracy, speed and computational requirements. The Plessy algorithm was found to have good stability and accuracy, but suffered from a large computational cost. The SUSAN method required the least computational resources and would therefore be suitable for implementation on a simple FPGA platform. However, it did not perform well on real world images. The Wang-Brady method was found to have better stability than SUSAN but worse than the Plessy algorithm while having a lower computational cost than Plessy and a higher cost than that for SUSAN. Despite the higher computational requirements, we conclude that the Plessy algorithm, because of its significantly better performance, is the most appropriate algorithm for hardware implementation.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".