Image-to-X Registration using Linear Features
Bibliographic record
Abstract
The registration of imagery to maps and GIS layers is a fundamental operation for the management of spatial data in GIS. This paper introduces automated algorithms for the registration of sequences of aerial imagery to vector map data using linear features (primarily roads) as control information. Our algorithms support both the use of single elements as well as complete networks. Regarding single elements, our method is based on the extraction of linear features using active contour models (a.k.a. snakes), followed by the construction of a polygonal template upon which a matching process is applied. To accommodate more robust matching, this work presents both exact and inexact matching schemes for linear features. Additionally, in order to overcome the influence of the snakes-based extraction process on the matching results, a matching refinement process is suggested. This information is used to generate image mosaics and register these mosaics to a map. The performance of the proposed scheme was tested on sequences of aerial imagery of 1 m resolution that were subjected to shifts and rotations using both the exact and inexact matching scheme, and was shown to produce angular accuracies of less than 0.7 degrees and positional accuracies of less than 2 pixels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".