A non-parametric statistics based method for generic curve partition and classification
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Bibliographic record
Abstract
Generic shape feature extraction is a challenging task for image and video content analysis. We present a non-parametric statistics based method for extracting generic shape tokens based on a Perceptual Curve Partition and Grouping (PCPG) model. In this PCPG model, each curve is made up of Generic Edge Tokens (GET) connected at Curve Partitioning Points (CPP). The types of GET and CPP provide a set of basic shape descriptors for semantic vocabulary. The new implementation of the PCPG is based on: 1) An arctangent space is employed to signify the evidence of CPPs at pixel-level. 2) The pixels' sequential order is taken as heuristic to establish a bin order preserving arctangent histogram for locating CPPs by examining the continuity of generic feature criteria statistically. 3) A new CPP detection scheme is capable of detecting CPPs and classifying GETs on the fly. Experiments are presented for performance demonstration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it