Difference of Circles Feature Detector
Bibliographic record
Abstract
Feature detection is a crucial step in many Computer Vision applications such as matching, tracking, visual odometry and object recognition, etc. Detecting robust features that are persistent, rotation-invariant, and quickly calculated is a major problem in computer vision. Feature detectors using the difference of Gaussian (DoG) are computationally expensive, however, if the DoG is used with image sub sampling at higher orders, the detectors become fast but their feature localization becomes inaccurate. Detectors based on difference of octagons (DoO) or difference of stars (DoS) algorithm are fast and localize the features accurately, but they are not rotation-invariant. This paper introduces a novel technique for the difference of circles (DoC) algorithm, used for feature detection, that is perfectly rotation-invariant and has the potential of being very fast through using circular integral images. The performance of DoC algorithm is compared with the difference of stars algorithm presented by 'Willow Garage'. The experiments conducted concentrate on the rotation-invariance property of DoC.
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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 | 0.000 | 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 | 0.000 | 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".