UNIVERSITY OF CALGARY Epipolar Resampling of Linear Array Scanner Scenes
Bibliographic record
Abstract
Normalized image generation (epipolar resampling) is an important task for automatic image matching. Normalized images facilitate the detection of feature correspondences in the images and therefore provide the advantages of reducing the search space as well as the matching ambiguities. Normalized image generation is a well-established procedure for images captured by frame cameras. Digital frame cameras that produce resolution and ground coverage comparable to those of analog aerial photographs are not yet available. Instead, linear array scanners can be used on aerial or space platforms in order to obtain such characteristics. The resulting scenes are formed by stitching the captured one-dimensional images that are produced as the sensor moves. Rigorous modeling necessitates accessing or estimating a large number of exterior orientation parameters of the images. The resulting epipolar lines are non-straight lines, which causes difficulties in epipolar resampling using the rigorous model. By comparison, the parallel projection model requires a smaller number of parameters, and it results in straight epipolar lines. In addition, as the flying height increases and the angular field of view decreases, similar to the case of space-borne scanners, the true perspective geometry can be approximated by parallel geometry. The mathematical models and the transformations related to the parallel projection model and its relation to the rigorous perspective projection model are developed. An approach for epipolar resampling of linear array scanner scenes based on the parallel projection model is established. Experimental results using synthetic as well as real data prove the feasibility of the developed approach. iii
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".