Segmentation of handguns in dual energy X-ray imagery of passenger carry-on baggage
Bibliographic record
Abstract
This paper considers three different methods for segmentation of the handguns from X-ray images of passenger carry-on baggage. (1) The simplest approach is a direct greylevel based segmentation in which a fixed absolute threshold and region growing are used to identify candidate regions of the image. This is a pixel-based approach which uses only the simplest region based information to validate a candidate area. It is however, computationally simple and highly reliable and robust. (2) The second method considered is the classical two label probabilistic relaxation labeling (PRL) technique. This method is a hybrid between a model-based segmentation and a pixel-based approach. (3) The third approach considered is a physical model in concert with significant line and edge determination applied on top of the PRL method. The method is computationally expensive. It may provide significant savings in later processing which will out weight the initial computational costs, however, this will require further augmentation of the object model.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".