Fast and accurate calibration-based thermal / colour sensors registration
Why this work is in the frame
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Bibliographic record
Abstract
Combination of thermal and electro-optical sensors is useful in numerous applications related to inspection and monitoring. A few manufacturers already offer hybrid thermal / colour cameras. However, those off-the-shelf products generally provide independent images from both sensors whereas an accurate pixel-by-pixel registration would be greatly beneficial for most applications. This paper presents a calibration-based approach allowing the acquisition of co-registered thermal / visible videos with a simple side-by-side camera configuration. The proposed method has the interesting capabilities of accurately registering both fields of view by a single image mapping More specifically, this mapping converts distorted image coordinates from thermal image to corresponding distorted image coordinates of colour image. Once computed, the projection matrix can be optimized for a specific object distance. An original calibration rig optimized for the thermal spectrum is also presented.
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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 | 0.001 |
| 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