Dual-correlation transformation for image stitching
Why this work is in the frame
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Bibliographic record
Abstract
In order to obtain accurate and stable image stitching results, we propose a stitching method for two images captured from different viewpoints based on correlation transformation. Aiming at resolving the limitation of the projective transformation that is commonly used in image stitching, a transformation called dual-correlation transformation is proposed in this paper. First, the estimation result of the fundamental matrix is calculated by the direct linear transformation based on the corresponding points in two images. Second, according to the presented dual-correlation transformation, a pair of correlation transformation matrices that are needed for dual-correlation warp can be obtained to realize the correspondence of each pixel in different images. Up to this stage, the method of image stitching based on transformation matrices has been accomplished. Finally, an optimization method based on factorization is especially proposed to solve the discontinuity problem that may occur in the dual-correlation warp. The experimental results and analyses show that the proposed method can achieve more accurate and natural stitching effects and has less computing time of the images in separate scenes compared with other similar methods.
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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 | 0.001 | 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 | 0.003 |
| Open science | 0.000 | 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 it