Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem
Bibliographic record
Abstract
<p>Registration of multi-modal images has been a challenging task<br />due to the complex intensity relationship between images. The<br />standard multi-modal approach tends to use sophisticated similarity<br />measures, such as mutual information, to assess the accuracy<br />of the alignment. Employing such measures imply the increase in<br />the computational time and complexity, and makes it highly difficult<br />for the optimization process to converge. The presented registration<br />method works based on structural representations of images<br />captured from different modalities, in order to convert the multimodal<br />problem into a mono-modal one. Two different representation<br />methods are presented. One is based on a combination of<br />phase congruency and gradient information of the input images,<br />and the other utilizes a modified version of entropy images in a<br />patch-based manner. Sample results are illustrated based on experiments<br />performed on brain images from different modalities.</p>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".