Occluded Leaf Matching with Full Leaf Databases Using Explicit Occlusion Modelling
Bibliographic record
Abstract
Matching an occluded contour with all the full contours in a database is an NP-hard problem. We present a suboptimal solution for this problem in this paper. We demonstrate the efficacy of our algorithm by matching partially occluded leaves with a database of full leaves. We smooth the leaf contours using a beta spline and then use the Discrete Contour Evaluation (DCE) algorithm to extract feature points. We then use subgraph matching, using the DCE points as graph nodes. This algorithm decomposes each closed contour into many open contours. We compute a number of similarity parameters for each open contour and the occluded contour. We perform an inverse similarity transform on the occluded contour. This allows the occluded contour and any open contour to be overlaid". We that compute the quality of matching for each such pair of open contours using the Fréchet distance metric. We select the best eta matched contours. Since the Fréchet distance metric is computationally cheap to compute but not always guaranteed to produce the best answer we then use an energy functional that always find best match among the best eta matches but is considerably more expensive to compute. The functional uses local and global curvature String Context descriptors and String Cut features. We minimize this energy functional using the well known GNCCP algorithm for the eta open contours yielding the best match. Experiments on a publicly available leaf image database shows that our method is both effective and efficient significantly outperforming other current state-of-the-art leaf matching methods when faced with leaf occlusion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".