<title>Segmentation of small vehicle targets in SAR images</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an algorithm for automatic segmentation of small vehicle targets in MSTAR images. The segmenter is based on a histogram threshold technique and is able to detect both target vehicles and their shadows, and it is divided into three parts. First, the main component of the pre-processing part is a morphological closing filtering which decreases the intensity of speckle in images. The second part of the segmenter performs a histogram threshold operation. It is built around the use of the EFC-based model selection algorithm to estimate an image histogram with a mixture of normal densities, and a new method to compute thresholds. In this paper, we introduce a new linear method for computing multi-level thresholds from a mixture of normal densities. The post-processing operation is performed in order to remove any small detected artefacts other than targets of interest.
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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 | 0.000 | 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 it