Visible and IR Data Fusion Technique Using the Contourlet Transform
Why this work is in the frame
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Bibliographic record
Abstract
In the last few years image fusion has gained considerable attention, where it can provide remarkable outputs for many image applications (i.e., detection of hidden objects). Images with different specifications (resolution, spectral, and spatial) can be fused to produce an output image that combines the best features of all input images. The quality of the output image varies based on the application. In this paper, a new region-based image fusion technique using the Contourlet Transform (CT) is proposed. The presented fusion approach combines the visual information from a visual colored image, and some information about the hidden objects from an IR image. The fused output image is better for human and machine interpretation, where it preserves the original chromaticity of the visual input image. The input images are segmented into small regions more suitable for the proposed algorithm. The segmentation process is performed in the frequency domain. The presented region-based fusion approach is more robust than the traditional pixel-based techniques, where it reduces: the blurring effects, sensitivity to the misregistration problem, and noise effects in the input images. Experimental results demonstrate the capability of the presented fusion technique in detecting hidden weapons and objects. Moreover, the algorithm preserves very high percentage of the input image's spectral components.
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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