基于Xception边缘检测器生成细粒度图案的矢量图形.
Anqi Chen1, Yicui Peng2, Meng Li2
1Chengdu Technological University, Chengdu, China.
PloS one
|June 11, 2025
概括
这项研究使用人工智能 (AI) 和机器学习从花刺图像中提取复杂的图案. 该方法有效地生成矢量图形,用于数字保存和对文化遗产的艺术重新释.
科学领域:
- 计算机视觉 计算机视觉
- 数字遗产是数字遗产的一部分.
- 模式识别 模式识别
背景情况:
- 非物质文化遗产 (ICH) 图像,就像江刺一样,经常具有细粒度的图案和复杂的边缘,这给传统的图像处理带来了挑战.
- 准确地提取这些图案对于数字保存和艺术重新释至关重要.
研究的目的:
- 利用人工智能 (AI) 开发一种创新的方法,从ICH图像中生成细粒度图案的矢量图形.
- 应用和评估机器学习算法,用于精确的边缘提取和图案识别在刺.
主要方法:
- 预处理技术,包括改进的自适应中间选 (IAMF) 和非局部平均选,用于降低花刺图案中的噪音.
- 使用Xception算法,一个卷积神经网络 (CNN),用于边缘检测和提取以创建矢量图形.
主要成果:
- 拟议的方法成功地消除了Qiang刺图像的颜色,通过边缘提取能够清晰识别图案形状特征.
- 实验结果证明了Xception算法的有效性,可以提取用于矢量图形生成的复杂模式.
结论:
- 这种由人工智能驱动的方法提供了一个有效的解决方案,可以将Qiang刺图案提取到二维矢量图形中.
- 该方法为各种ICH图像的数字保存和艺术重新解释提供了可靠的参考.
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