Efficient and reliable methods for direct parameterized image registration
Bibliographic record
Abstract
This thesis examines methods for efficient and reliable image registration in the context of computer vision and medical imaging. Direct, parameterized image registration approaches work by minimizing a difference measure between a fixed reference image, and the image warped to match it. The calculation of this difference measure is the most computationally intensive part of the process and for faster registration it either has to be calculated faster, or calculated fewer times. Both possibilities are addressed in detail. Efficiency and reliability are addressed in four ways (1) Methods are presented for generalizing the Gauss-Newton Hessian approximation to the non-least squares case, and for the optimal selection of scaling factors for the transformation parameters. Both of these enhance performance by enabling optimization algorithms to perform fewer evaluations of the difference measure. The performance of a wide range of optimization algorithms is analyzed both theoretically and experimentally, and guidelines are presented for optimizer selection based on the characteristics of the registration problem. (2) Using only a portion of the available pixels results in faster calculation but suffers from a potential loss of accuracy. An algorithm is presented which applies formal deliberation control methods to managing this tradeoff. By managing the amount of image data used at every evaluation of the cost function, the algorithm adapts to the nature of the images and the stage of the optimization. This adaptive approach allows greater efficiency without sacrificing reliability. (3) It is shown that the scale used to compute the derivative is a critical factor to consider when selecting subsets of pixels for registration, that has largely been ignored in previous work. Finally, (4) two existing efficient registration approaches, the inverse compositional, and efficient second order algorithms, rely on specialized optimizer update steps and specialized pa
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".