Mapping the Problem Space of Image Registration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we explore a conceptual mapping of the image registration problem into an N-Dimensional problem space based on the properties of the images being registered, in contrast to traditional surveys of image registration which divide the field algorithmically. The five main dimensions of our proposed mapping are variations in: spatial alignment, intensity, focus, sensor type, and structure. Individual algorithms can be thought of as supporting a volume of solutions within the problem domain map, although they are typically designed to solve problems along a single dimension. Existing image registration papers and techniques are taxonomized within this mapping according to these major dimensions. The focus of this paper is threefold. First, an up-to-date survey of image registration techniques is provided, building from previous seminal surveys. Second, a novel taxonomy is presented that organizes the registration problem space based on the variation between the images being registered. Finally, a number of new research areas made possible under this novel taxonomy are examined, and a path is laid out for future research in the field.
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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 | 0.000 |
| 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 it