Elastic image registration using subspace constraints
Bibliographic record
Abstract
Image registration is the process of aligning two images taken from different views, at different times, or by different modalities. In this article, we propose a new framework that incorporates prior deformation knowledge in the registration process. First, an elastic image registration method is used to obtain deformation fields by modeling the nonrigid deformations as locally affine and globally smooth flow fields. Next, the estimated geometric transformation maps are used to train a prior deformation model using two subspace projection techniques, namely principle component analysis (PCA) and independent component analysis (ICA). A smooth deformation is now guaranteed by projecting the locally calculated deformation onto a subspace of allowed deformations. One advantage of our approach is in its ability to guarantee smoothness without the need for iterative regularization. The new algorithms were validated using the Amsterdam library of images (ALOI). Our experiments demonstrate promising results in terms of mean square error.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 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".