Feature Matching for Aligning Historical and Modern Images
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
Provision of historical information based on geographical location represents a new scope of connecting the present of a certain location or landmark with its history through a timescape panorama. This may be achieved by exploring a linear timeline of photos for certain areas and landmarks that have both historic and modern photos. Matching modern to historical images requires a special effort in the sense of dealing with historical photos which were captured by photographers of different skills using cameras from a wide range of photographic technology eras. While there are many effective matching techniques which are vector- or binarybased that perform effectively on modern digital images, they are not accurate on historic photos. Photos of different landmarks were gathered on a wide ranging timeline taken in different conditions of illumination, position, and weather. This work examines the problem of matching historical photos with modern photos of the same landmarks with the intent of hopefully registering the images to build a timescape panorama. Images were matched using standard vector-based matching techniques and binary-based techniques. Match results of these sets of images were recorded and analysed. Generally, these results show successful matching of the modern digital images, while matching historic photos to modern ones shows poor matching results. A novel application of a hybrid ORB/SURF matching technique was applied in matching modern to historic images and showed more accurate results and performs more effectively.
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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 | 0.001 | 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