Image registration using the Hausdorff fraction and virtual circles
Bibliographic record
Abstract
Image registration is the process of determining the transformation which best matches, according to some similarity measure, two images of the same scene taken at different times or from different view points. We propose a new image registration method based on the similarity measure called Hausdorff fraction and a novel set of features called virtual circles. This method is guaranteed to find the best homothetic transformation, if only two virtual circles are preserved between the model and the scene. Another advantage of this method is that it is a general method that works well for most types of images. The time complexity of this method is O(n/sup 2/ +nmE/sub m/), where n and m are the number of virtual circles in scene and model respectively, and E/sub m/ is the number of edge points in the model. However, using an heuristic called circularity criterion, the number of virtual circles can be reduced allowing for faster execution times, without much loss in robustness.
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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 | 0.001 |
| 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".