An efficient implementation of affine transformation using one-dimensional FFTs
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Bibliographic record
Abstract
In this paper, we propose a new decomposition scheme and an efficient interpolation algorithm for affine transformation of a digital image. We try to reconstruct the affine-transformed image by resampling it with the highest possible quality, lowest complexity and throughput rate. Based on the proposed decomposition, the transform is completed by a sequence of 3-pass translations and a scaling operation where each of them is one-dimensional in nature. This method preserves quality and guarantees simplicity. We place the emphasis on the feasibility of a parallel implementation that can benefit from pipeline technologies. Further, an efficient FFT-based implementation of this new algorithm is suggested. Experimental evidence of the effectiveness and robustness of the proposed method is reported. The problem is relevant to video transmission, image registration, and computer graphics manipulation.
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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 | 0.001 |
| 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