Study on the Possibility of Combining Abrasive Painting Technique with Algorithmically Generated Color Distribution Methods in Lacquer Painting Creation
Bibliographic record
Abstract
As a conventional technique in lacquer painting, the abrasion painting technique is widely used in the creation of modern lacquer painting.In order to promote the digital innovation of the abrasion painting technique in the creation of lacquer paintings, a fusion scheme of the abrasion painting technique and color distribution in the creation of lacquer paintings is formulated.According to the relationship between color and gray scale, the color mapping of image coloring algorithm is proposed under the framework of energy optimization algorithm to realize algorithm-driven lacquer painting color generation.In addition, with the technical support of the renderer, the color distribution of lacquer paintings is integrated with the milling technique according to the principle of texture mapping.With the help of evaluation indexes and experimental platforms, we simulate and analyze the techniques and colors in lacquer painting.In the color generation of lacquer paintings, the indicators of this paper's method are 34.09,0.964, 0.025 and 4.28 in order, which veri ies the application effect of this paper's method in the color generation of lacquer paintings.In addition, the speed of this paper's rendering method (42-86FPS), fully meets the requirements of real-time drawing, this method better promotes the fusion of grinding and painting techniques and color distribution in the creation of lacquer paintings, which is of great signi icance to the digital dissemination of traditional culture of non-heritage.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".