Construction of Shadow Model by Robust Features to Illumination Changes
Why this work is in the frame
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Bibliographic record
Abstract
Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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