Registration of Modern and Historic Imagery for Timescape Creation
Bibliographic record
Abstract
The creation of temporal of panoramic visualization (timescapes) of historic landmarks given the available photos on the Internet is a challenging problem. Dealing with hundreds of thousands of modern and historic photos captured under varying conditions not only has the typical image registration problems, but suffers greatly from multisensor, multitemporal, and multispatial capturing attributes. As well, imprecise lenses and digitization techniques used to generate digital versions of historic images only increases the difficulty of the registration problem. An automated processing of collected photos of many landmarks around the world is presented in this paper and offers a novel view of these registered images using the timescapes concept of merging historical and location information into a single scope. Registering both historic and modern photos in an accurate and precise manner allows an opportunity to support the cultural heritage preservations of such landmarks. This paper presents a timescape display through registered images of landmarks in a single attractive temporal and spatial panoramic view.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".