Multi-Resolution Image Registration Using Multi-Class Hausdorff Fraction
Bibliographic record
Abstract
Recently, a new image registration method, based on the Hausdorff fraction and a multi-resolution search of the transformation space, has been developed in the literature. This method has been applied to problems involving translations, translation and scale, and Affine transformations. In this paper, we adapt the above method to the set of similarity transformations. We also introduce a new variant of the Hausdorff fraction similarity measure based on a multi-class approach, which we call the Multi-class Hausdorff Fraction (MCHF). The multi-class approach is more efficient because it matches feature points only if they are from the same class. To validate our approach, we segment edge maps into two classes which are the class of straight lines and the class of curves, and we apply the new multi-class approach to two image registration examples, using synthetic and real images, respectively. Experimental results show that the multi-class approach speeds up the multi-resolution search algorithm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".