def updata(num):

return [point\_ani];

interval=num).

import matplotlib.pyplot as plt from matplotlib; import FuncAnimation as animation class;

point\_ani=plt.plot(x1, y1, 'point1', x2, y2, 'point2', ...);

**Figure 11.** Feature pixel point sampling and optimization based on image brightness feature map. (**a**) Brightness feature map. (**b**) Initial sampling. (**c**) Sampling optimization.

#### 2. Creating feature graphics;

The brightness feature map is sampled and optimized, where the sampling interval *s* = 2, the number of samples *n* = 56, and the optimal spacing *l* = 4 pixel units. The preliminary sampling result is shown in Figure 11b and the optimized sampling result is shown in Figure 11c. The specific process is that we take the progressive scanning and interval sampling to the image brightness feature map output from the proposed model with the sampling number *n* = 56 and the sampling interval *s* = 2. Then, we select the pixel points if their brightness value is higher than the preset brightness threshold *θ*<sup>0</sup> = 150 and output them to form the brightness feature sampling matrix. Further optimization is conducted with the optimized spacing *l* = 4. If the space between the sampling points is less than *l* = 4 pixel units, we reserve the pixel point with min x + y to form the key feature point matrix. At last, the drawMatches() method is used to connect the optimized sampling points whose spacing is less than the set value *l*<sup>0</sup> = 6, and the feature graphics is formed, which consequently shows that the lines are partially smoothed.

#### 3. Generation of dynamic feature graphic.

Dynamic feature graphic is generated by the generated method of dynamic graphic mentioned above, as shown in Figure 12. In the process of generating dynamic graphic, the key frame feature points are the middle points in each line. By using this method, dynamic feature graphic is generated, which can be applied to design the intelligent graphical interface. The displayed effect of this dynamic graphic shows that the better dynamic line graphics are obtained after some smoothing, which can accurately convey the image features of traditional engraving graphics and bring users to be a good visual experience.

The practical results show that the image feature extracted method proposed in this paper can effectively extract the image features of traditional engraving graphics. The feature or dynamic feature graphics created by the proposed method about the intelligent graphical design can be applied to the intelligent application interface, which can convey the image features of traditional engraving graphics well.

**Figure 11.** Feature pixel point sampling and optimization based on image brightness feature map.

Dynamic feature graphic is generated by the generated method of dynamic graphic mentioned above, as shown in Figure 12. In the process of generating dynamic graphic, the key frame feature points are the middle points in each line. By using this method, dynamic feature graphic is generated, which can be applied to design the intelligent graphical interface. The displayed effect of this dynamic graphic shows that the better dynamic line graphics are obtained after some smoothing, which can accurately convey the image features of traditional engraving graphics and bring users to be a good visual

(**a**) Brightness feature map. (**b**) Initial sampling. (**c**) Sampling optimization.

3. Generation of dynamic feature graphic.

**Figure 12.** Dynamic graphic of square shape lines after smoothing. (**a**) Dynamic graphic at frame 1. (**b**) Dynamic graphic at frame 4. (**c**) Dynamic graphic at frame 7. (**d**) Dynamic graphic at frame 10. (**e**) Dynamic graphic at frame 13. (**f**) Dynamic graphic at frame 15. **Figure 12.** Dynamic graphic of square shape lines after smoothing. (**a**) Dynamic graphic at frame 1. (**b**) Dynamic graphic at frame 4. (**c**) Dynamic graphic at frame 7. (**d**) Dynamic graphic at frame 10. (**e**) Dynamic graphic at frame 13. (**f**) Dynamic graphic at frame 15.

#### The practical results show that the image feature extracted method proposed in this paper can effectively extract the image features of traditional engraving graphics. The fea-**6. Conclusions**

experience.

ture or dynamic feature graphics created by the proposed method about the intelligent graphical design can be applied to the intelligent application interface, which can convey the image features of traditional engraving graphics well. **6. Conclusions** In this study, we present the image feature extracted method, in which the image features are extracted by convolution operation after a series of image brightness feature processing. On the basis of the brightness feature map output from the proposed model, feature graphics are designed and applied on the intelligent graphical interface. Theoretical and practical research shows that the extracted model has a significant effect on the performance of the image features extracted from traditional engraving graphics. Moreover, the effect of image feature extraction is related to model parameters, which further affect the feature graphic design. The advantage of this method is that the brightness feature processing greatly simplifies the process of image feature extraction, and the extracted accuracy is improved by convolution operation. Take the pottery engraving In this study, we present the image feature extracted method, in which the image features are extracted by convolution operation after a series of image brightness feature processing. On the basis of the brightness feature map output from the proposed model, feature graphics are designed and applied on the intelligent graphical interface. Theoretical and practical research shows that the extracted model has a significant effect on the performance of the image features extracted from traditional engraving graphics. Moreover, the effect of image feature extraction is related to model parameters, which further affect the feature graphic design. The advantage of this method is that the brightness feature processing greatly simplifies the process of image feature extraction, and the extracted accuracy is improved by convolution operation. Take the pottery engraving graphics from the Neolithic Age as an example, the practice of the image feature extraction and intelligent dynamic graphic design is carried out, which further verify the effectiveness of the proposed method, especially in the field of intelligent feature graphic design and application. However, due to the limit of the sample amount and experiment condition, the image feature extracted model needs to further improve the extracted accuracy. The design efficiency of intelligent graphics needs to be further enhanced by undergoing more practice. More in-depth and extensive research needs to be taken in the next work.

**Author Contributions:** Conceptualization, Y.L.; methodology, Y.L.; software, Y.L. and Y.T.; validation, Y.L. and Y.T.; data curation, Y.L.; writing—original draft preparation, Y.L.; writing—review and editing, Y.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The datasets supporting the conclusion of this article are included within the article.

**Acknowledgments:** This work is supported by the Key Research Project of Humanities and Social Sciences in Colleges and Universities of Anhui Province (no.SK2019A0121); Support Program for Outstanding Young Talents in Colleges and Universities in Anhui Province (no. gxyq2020166); Scientific Research Project of Anhui Polytechnic University (no.Xjky2020115); Natural Science Research Project of Institutions of Higher Education in Anhui Province of China (no. KJ2017A114); National Natural Science Foundation of China (no. 11902001); Middle-aged Top-notch Talent Support Programs of Anhui Polytechnic University.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

