Locally Adaptive Thresholding for Single-Shot Structured Light Patterns
Why this work is in the frame
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Bibliographic record
Abstract
Image thresholding is a challenging task due to its sensitivity to environmental variations and degradation in the quality of the captured image. Although many image thresholding methods have been introduced, most of them require the fine tuning of a thresholding parameter that is not suitable for single-shot structured light (SSSL) based projector-camera applications. In this paper, we introduce a locally adaptive thresholding method with automatic parameter selection based on the local statistics of the distinct image partitions. For assessing the proposed scheme, we introduce an evaluation that relies on mapping SSSL patterns between the camera and projector spaces. Experimental results demonstrate the effectiveness of the proposed technique by maintaining the thresholding accuracy of the baseline method, without the need to fine tune the obtained thresholding parameter or any noticeable change in the qualitative results.
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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 | 0.001 | 0.001 |
| 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