Feature Matching for Aligning Historical and Modern Images
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".