Local self-similarity as a dense stereo correspondence measure for themal-visible video registration
Bibliographic record
Abstract
The robustness of Mutual Information (MI), the most used multimodal dense stereo correspondence measure, is restricted by the size of the matching windows. However, obtaining the appropriately sized MI windows for matching thermal-visible pair of images of multiple people with various poses, clothes, distances to cameras, and different levels of occlusions is quite challenging. In this paper, we propose local self-similarity (LSS) as a multimodal dense stereo correspondence measure. We integrated LSS as a similarity metric with a disparity voting registration method to demonstrate the suitability of LSS for a visible-thermal stereo registration method. We have analyzed comparatively LSS and MI as multimodal correspondence measures and discussed LSS advantages compared to MI. We have also tested our LSS-based registration method in several indoor videos of multiple people and shown that our registration method outperforms the most recent MI-based registration method in the state-of-the-art.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".