Bibliographic record
Abstract
With the cost decline of Infrared (IR) cameras, it is envisage that more IR camera will be deployed in vision surveillance system for around-clock day and night surveillance. Infrared camera sense radiation emitted by an object at a non-zero absolute temperature in the infrared spectrum which is not available in visual image, but lose information, such as texture, color and geometric, which is available in visual cameras. Fusion of IR and visual images can enhance features in both kinds of images. And more impressively, it can reveal some new features that might not be present either in IR images or in visual images. In this paper, a statistical signal processing approach based on expectation maximization (EM) is proposed for IR and visual image fusion. The sensor images are described as the true scene corrupted by additive Gaussian distortion. At each iteration of the EM, the fusion result is obtained by using covariance intersection (CI) in the E-step, while the model parameters are updated in the M-step. The simulation results using the real IR and visual images demonstrate the effectiveness of the proposed method.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".