Joint histogram between color and local extrema patterns for object tracking
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Bibliographic record
Abstract
In this paper, a new algorithm meant for object tracking application is proposed using local extrema patterns (LEP) and color features. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray level values. The proposed method differs from the existing LBP in a manner that it extracts the edge information based on local extrema between center pixel and its neighbors in an image. Further, the joint histogram between RGB color channels and LEP patterns has been build which is used as a feature vector in object tracking. The performance of the proposed method is compared with Ning et al. on three benchmark video sequences. The results after being investigated proposed method show a significant improvement in object tracking application as compared to Ning et al.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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