Line‐based modified iterated Hough transform for automatic registration of multi‐source imagery
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
Abstract Image registration aims at combining imagery from multiple sensors to achieve higher accuracy and derive more information than that obtained from a single sensor. The enormous increase in the volume of remotely sensed data that is being acquired by an ever‐growing number of earth observation satellites mandates the development of accurate, robust, and automated registration procedures. An effective automatic image registration has to deal with four issues: registration primitives, transformation function, similarity measure, and matching strategy. This paper introduces a new approach for automatic image registration using linear features as the registration primitives. Linear features have been chosen because they can be reliably extracted from imagery with significantly different geometric and radiometric properties. The modified iterated Hough transform (MIHT), which manipulates the registration primitives and similarity measure, is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function as well as the correspondence between conjugate primitives. The MIHT procedure follows an optimal sequence for parameter estimation that takes into account the contribution of linear features with different orientations at various locations within the imagery towards the estimation of the transformation parameters in question. Experimental results using real data proved the feasibility and robustness of the suggested approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it