Study on Elimination Algorithms for Line Segment Mismatches
Bibliographic record
Abstract
Image matching is a key step for remotely sensed image registration and digital elevation model (DEM) generation. Compared with point matching, few studies have focused on line matching for images, especially elimination algorithm of mismatched line segments. Therefore, this work systematically studies elimination algorithms of line segment mismatches by combining 2 transformation models (i.e., affine and homography) with 2 M-estimators or 2 sample consensus methods (i.e., random sample consensus, RANSAC, and least median of squares, LMedS). The main idea is as follows. After line segments are extracted and matched, the proposed algorithms can automatically remove mismatched line segments based on an error function of line segment. Aerial images with panchromatic bands and standard false color synthesis were selected for testing. Experiments were performed to compare different combinations of these models and methods and to quantitatively evaluate the performance of the algorithms in terms of accuracy and run time. The results show that the proposed algorithm can be effectively applied to automatically eliminate mismatched line segments, and among all combinations the homography model with LMedS performs the best. The algorithm can also ensure and control the quality of line segment matching from stereo pairs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".