Research of Distortion Target Recognition Based on Minimum Average Correlation Energy Filters
Why this work is in the frame
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Bibliographic record
Abstract
One of the bottleneck techniques of correlation pattern recognition is the accurate detection of distortion targets such as rotation and scale.Through researching on the variety algorithms of distortion target recognition,the minimum average correlation energy(MACE) filter used in matched filter was modified in this paper.Firstly,based on the basic idea of MACE filter,the filter function was constructed.Then,the edge extraction of training images was performed before composing reference images.Lastly,Laplace sharpening for the joint power spectrum of joint image was used.In this way,the rotated target image was recognized in joint transform correlator(JTC)successfully,and the correlation peak brightness of distortion images is increased,and the range of detection and recognition is improved.And also,as a practical example,the computer simulation experiments and optical experiment of a rotated aircraft target image were carried out,proving the feasibility of this algorithm.
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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 | 0.001 |
| 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 | 0.000 |
| 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