Benefits of hybrid DCT domain image matching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An enhancement to least squares image matching is proposed which combines a Discrete Cosine Transform (DCT) domain solution of the linearized normal equations, and resampling between iterations in the pixel domain. This approach reduces the size of the normal equations by discarding higher frequency DCT coefficients, while avoiding the overhead of image resampling in the DCT domain. A method for computing the DCT of the sampled derivative of a function from the DCT of its samples is given, and the least squares problem is framed in the DCT domain. In an experimental comparison between the proposed algorithm and an equivalent pixel domain algorithm, we find that the match time can be halved for 32 × 32 pixel windows, and reduced to 75% for 16 × 16 windows, while measures of match quality remain comparable or improve. The measures of much quality considered were the mean and standard deviation of the disparity error, and the number of match windows that converged. The optimum percentages of DCT coefficients for these window sizes were 20% for the 16 × 16 window and 10% for the 32 × 32 window. An 8 × 8 window size was also tested, but showed no speed-up over the pixel domain algorithm. The approach incorporates derivative estimates that result in better accuracy than can be achieved using the first differences of a pixel domain approach.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it