PHOTOGRAMMETRIC EXPLOITATION OF HDR IMAGES FOR CULTURAL HERITAGE DOCUMENTATION
Bibliographic record
Abstract
Abstract. Basic goal of this project is to investigate and therefore highlight the usefulness of High Dynamic Range Images for photogrammetric applications in the field of Cultural Heritage Documentation. Scenes with High Dynamic range – difference between the brightest and the darkest parts – is impossible to be recorded without loss of details and texture in dark areas (due to underexposure) and in bright areas (due to overexposure) because of digital sensor's limitation in high dynamic range recording. In digital recording, the most recent and effective solution is High Dynamic Range Images (HDRI). These images are created by merging multiple images of the same scene, each of which has been taken with different shutter speed and thus providing a better range of images with different exposures. An HDR image alone is overcoming the loss of information caused by unfavorable lighting conditions. In photogrammetric applications, images have to be of high quality and represent faithfully the scene they depict. For applications of Cultural Heritage Documentation, where during image acquisition lighting conditions might be difficult, HDR technology can positively contribute to the acquisition of images of better quality and, consequently, to the creation of orthophotos with no radiometric problems. In this paper, a detailed reference to HDRI technology is made and also the geometric reliability and photogrammetric applicability of HDR images is examined and confirmed. In addition, an example of photogrammetric application in Cultural Heritage Documentation is presented and evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".