A real time pixel-level based image fusion via adaptive weight averaging
Bibliographic record
Abstract
A novel pixel-level image fusion scheme for thermal and visual images is presented. The image fusion technique rests on physical characteristics of targets deemed of interest in a surveillance scenario. Each picture element (pixel), in both the thermal and visual images, is assigned a weight proportional to the interest associated with it. Interest is defined as "not natural" or "man-made". A weighted average of the intensity images representing the thermal and visual modalities is then performed for every corresponding pair of visual and thermal picture elements to obtain the fused image. For the thermal images, elements that are warmer or cooler than their environment (background) are deemed to be of "interest". To this end, the thermal weights are associated with the divergence of the intensity of these pixels from the image mean intensity. For the visual images, the facts that the "targets of interest" are usually larger than the instantaneous field of view (IFOV) of the visual sensor and have a reflection behaviour that is more specular are used. The visual weight determination is based on the local variance in space and time of the intensity of the visual pixels, The performance of this technique is compared to a number of existing techniques in the literature. The results reveal that the proposed technique performs better than those in the literature. In addition, it also reveals that the proposed technique is more robust than those in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".