Adaptive Infrared Target Segmentation Algorithm Based on Particle Filter
Why this work is in the frame
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Bibliographic record
Abstract
A novel algorithm on infrared target extraction based on Sequential Monte Carlo Algorithm is proposed in this paper. We analyzed and solved the problem of the infrared target segmentation in the view of state estimation, and computed the threshold value adaptively by optimal estimation of a dynamic system. In the framework of Particle Filter, the threshold state space is established on the Gray-variance Weighted Information Entropy and the gray value of each pixel. The state transition model is chosen as random-drift model. As for the observation probability model, a novel objective function, integrating gray, entropy, gradient and spatial distribution of pixels, is proposed for both the quantitive evaluation of the segmentation and the weight of each particle in the particle set. Finally, the estimation for segmentation threshold is the weighted average of all the particles. The experimental results show the effectiveness of the proposed algorithm.
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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