Fourier transform-based pattern design method and geometric deformation in lacquer decorative design
Bibliographic record
Abstract
In order to optimize the pattern design method in lacquerware decoration design, this paper first analyzes the discrete and continuous situation of the pattern in time and frequency by Fourier transform method, and explains the mapping principle of Fourier variation.After that, the original image is processed such as sharpening and smoothing under the Fourier transform algorithm, and the lacquer decorative pattern after automatic deformation is obtained through interaction on the basis of 2D affine transformation technology.Finally, the geometric deformation of the lacquer decoration design from 2D to 3D is simulated and verified.The results show that in this paper, the threshold value, brightness and contrast of the lacquer decorative design patterns can be obtained by the geodesic distance deformation algorithm under the Fourier transform in MATLAB software to get the geometric patterns of the lacquer decorative design with the main color of the appropriate filler blocks.The corresponding blue values of the four patterns are 418, 38, 104 and 256; the optimal values of green are 256, 100, 87 and 405; and the optimal values of red are 256, 57, 63 and 117.3-D imaging simulation experiments show that the average absolute error, root mean square error and maximum absolute error of the depth of the geometric patterns of the 3-D imaging method and the geometric patterns proposed in this paper are all significantly reduced, and the depth of the geometric patterns in the 20mm depth range are reduced significantly.and the advantages of this paper's method are more obvious in the depth variation range of 20mm.It can be seen that the algorithm of this paper can improve the deformation effect of geometric patterns in lacquer decorative design.
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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 | 0.003 | 0.000 |
| 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 | 0.000 | 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 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".