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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".