A new fusion algorithm for shadow penetration using visible and midwave infrared polarimetric images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a new polarimetric image fusion algorithm to discriminate objects lying in shadow areas against cluttered backgrounds. Polarimetric signatures of man-made objects are collected using a fully automated passive polarimetric sensor-suite operating in the visible, shortwave, midwave, and longwave infrared bands. The polarization state of the radiation emitted and/or reflected from objects' surfaces and surrounding background is characterized using the total intensity, the degree of linear polarization, and the phase of the polarization. Using two distinct scenarios, experimental results demonstrate the utility of the proposed image fusion algorithm to exploit the polarized signatures of man-made objects in the visible and midwave infrared bands for shadow penetration purposes.
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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