Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction
Bibliographic record
Abstract
Infrared thermal image makes the target have certain degree of recognition by reflecting the thermal radiation information emitted by the target, which effectively compensates the information loss of visible light image in harsh imaging environment.Contour extraction effect of target area using traditional Canny algorithm is not good, because the contour gradient change of infrared thermal image target area is not obvious.At the same time, the threshold of most of algorithms needs to be set manually, which is greatly affected by subjective factors, and the image processing efficiency is low.Therefore, this paper studied the target area extraction algorithm of infrared thermal image by combining target detection with matching correction.First, the paper introduced the feature matching algorithm based on grid motion statistics, and converted smoothness constraint of motion into statistics, thus replacing the number of extended feature points with the acquisition of features with better performance and filtering false image matching based on the number of other matching points in the neighborhood of statistical matching points.Second, based on the feature matching results obtained in the previous section, this paper proposed a extraction method of infrared thermal image target area based on thermal feature descriptors, which combined the extracted thermal features with the semantic attributes of each area in the infrared thermal image, thus distinguishing the subtle differences between the infrared thermal image sub-categories.Finally, experimental results verified 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".