A divide-and-conquer approach to contour extraction and invariant feature analysis
Bibliographic record
Abstract
A novel method to process and analyse spatial information is presented in this paper. The spatial information, such as geometric shapes, object boundaries and trajectories, is extracted as a discrete sequence of points from images obtained through remote sensing. The algorithm generates contour point sequences and then uses a scale invariant analysis to extract invariant arc features. These arc features are subsequently used for object identification and recognition, as well as for image matching. The algorithm detects corner-like features in the presence of low curvature, sharp noise and discretization (spatial quantization), typical for images obtained by aerial photography, digital map scanning, satellite imaging or other remote sensing image acquisition techniques. The resulting feature vectors can be used for stable and robust object feature analysis and object detection. Experimental analysis confirms the efficiency and robustness of this method, using dataset consisting of varied shapes with considerable noise and ambiguity. The method allows not only stable feature detection, but also general shape analysis through identifying convexity, linearity and curvature properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".