Relative Position φ-Descriptor Computation for Complex Polygonal Objects
Bibliographic record
Abstract
As a part of regular conversation, one often will refer to the spatial relationships between objects by way of their positioning relative to each other. Relative Position Descriptors (RPDs) are a type of image descriptor tuned to extract this spatial relationship information from pairs of objects within an image. Of the existing RPDs, the Φ-descriptor encapsulates the widest variety of spatial relationships. Currently, algorithms exist for its computation in the case of both 2D raster and 2D vector objects. However, the algorithm for its calculation in the 2D vector case can only handle pairs of simple polygons and lacks some key features, including support for objects made of disjoint parts, objects with holes, objects sharing vertices or with parallel overlapping edges, and various spatial relationships. This thesis presents an approach for complex polygonal object Φ-descriptor computation built upon the previous vector approach. The new algorithm breaks the problem down into the analysis of object boundaries, polygon edges that represent changes in the membership of objects and spatial relationships, and brings it more in-line with the design of the 2D raster approach.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".