<title>Online database updating by change detection</title>
Bibliographic record
Abstract
Synthetic vision systems render artificial images of the world based on a database and position/attitude information of the aircraft. Due to both its static nature and inherent modelling errors, the database introduces anomalies in the synthetic imagery. Since it reflects at best a nominal state of the environment, it often requires updating via online measurements. The latter can vary from correction of pose and geometry to more complex operations such as marking the locations of detected obstacles. This paper presents an approach for detecting database geometric anomalies online. Since range sensors have a low update rate, they cannot be used for quick validation. Instead of range data, the proposed technique employs an imaging sensor, which can be of any type. It takes advantage of the fact that given a geometric model of the scene and known motion of the observer, the sensor image warping can be exactly predicted. If the geometry of the database is incorrect, the sensor image will not be correctly predicted and geometric differences will thus be detected. The algorithm is tested against simulated imagery and results show that it can correctly identify geometric anomalies.
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 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 | 0.001 | 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 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".