Unconstrained license plate detection using the Hausdorff distance
Why this work is in the frame
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Bibliographic record
Abstract
This paper reports on a new technique for unconstrained license plate detection in a surveillance context. The proposed algorithm quickly finds license plates by performing the following steps. The image is first preprocessed to extract the edges; opening with linear structuring elements ensures that plate sides are enhanced. Multiple scans using the Hausdorff distance are made through the vertical edge map with binary templates representing a pair of vertical lines (with varying gap to account for unknown plate size), so they efficiently pinpoint areas in the image where plates may be located. Inside those areas, the Hausdorff is used again, this time over the gradient image and with a family of templates corresponding to rectangles which have been subjected to geometric transformations (to account for perspective effects). The end result is a set of plate location candidates, each associated to a confidence level that is a function of the quality of match between the image and the template. An additional criterion based on the symmetry of plate shapes also supplies complementary information about each hypothesis that allows rejection of many bad candidates. Examples are given to show the performance of the proposed method.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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