Fast and Robust Intensity-Based Image Matching Using FFT
Bibliographic record
Abstract
This paper presents a fast and robust image matching approach based on the cosine M-estimator kernel and the Fast Fourier Transform (FFT). We show that the robust cosine M-estimator can be effectively used to compare image intensities through correlation of transformed images. The speed of the method derives from the use of FFT to compute correlation. Its computational complexity is O (N log N), compared to O(N²) for direct matching. Experimental results demonstrate that the proposed method maintains high matching accuracy even in the presence of up to 60% outliers and 60% occlusion. It outperforms traditional approaches such as Sum of Squared Differences (SSD) and Normalized Cross-Correlation (NCC). Moreover, the proposed approach achieves superior performance compared to recent deep learning-based methods such as LoFTR. By combining the cosine M-estimator with FFT, the method is suitable for real-time applications that require robust matching under challenging conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".