A FRAMEWORK FOR AN AUTOMATIC SEAMLINE ENGINE
Bibliographic record
Abstract
Seamline generation is a crucial last step in the ortho-image mosaicking process. In particular, it is required to convolute residual geometric and radiometric imperfections that stem from various sources. In particular, temporal differences in the acquired data will cause the scene content and illumination conditions to vary. These variations can be modelled successfully. However, one is left with micro-differences that do need to be considered in seamline generation. Another cause of discrepancies originates from the rectification surface as it will not model the actual terrain and especially human-made objects perfectly. Quality of the image orientation will also contribute to the overall differences between adjacent ortho-rectified images. Our approach takes into consideration the aforementioned differences in designing a seamline engine. We have identified the following essential behaviours of the seamline in our engine: 1) Seamlines must pass through the path of least resistance, i.e., overlap areas with low radiometric differences. 2) Seamlines must not intersect with breaklines as that will lead to visible geometric artefacts. And finally, 3), shorter seamlines are generally favourable; they also result in faster operator review and, where necessary, interactive editing cycles. The engine design also permits alteration of the above rules for special cases. Although our preliminary experiments are geared towards line imaging systems (i.e., the Leica ADS family), our seamline engine remains sensor agnostic. Hence, our design is capable of mosaicking images from various sources with minimal effort. The main idea behind this engine is using graph cuts which, in spirit, is based of the max-flow min-cut theory. The main advantage of using graph cuts theory is that the generated solution is global in the energy minimization sense. In addition, graph cuts allows for a highly scalable design where a set of rules contribute towards a cost function which, in turn, influences the path of minimum resistance for the seamlines. In this paper, the authors present an approach for achieving quality seamlines relatively quickly and with emphasis on generating truly seamless ortho-mosaics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".