用医学影像追踪技术生成仿画笔刷，让图像渲染更自然。
Applying Medical Imaging Tractography Techniques to Painterly Rendering of Images
- 用结构张量替代梯度，获取更精准的图像方向信息
- 通过轨迹追踪算法生成符合艺术风格的笔触分布
- 适合对艺术化图像处理感兴趣的开发者与研究者
医生和研究人员常使用扩散张量成像（DTI）和轨迹追踪技术来可视化人体组织的纤维结构。本文探索了这些技术与图像绘画风格化之间的联系。所提出的方法利用轨迹追踪算法，生成模仿人类艺术家作画过程的笔触，类似于在DTI中追踪纤维的方式。图像方向的类比于扩散张量的是结构张量，相比仅依赖梯度，能提供更优的局部方向信息。该方法在人像和一般图像上进行了演示，并讨论了纤维追踪与笔触布局之间的类比关系，以轨迹追踪的语言进行表述。这项工作是对扩散张量成像技术跨领域应用于图像风格化渲染的一次探索性研究。所有代码已开源：https://github.com/tito21/st-python。
原文摘要 · Abstract (English)
Doctors and researchers routinely use diffusion tensor imaging (DTI) and tractography to visualize the fibrous structure of tissues in the human body. This paper explores the connection of these techniques to the painterly rendering of images. Using a tractography algorithm the presented method can place brush strokes that mimic the painting process of human artists, analogously to how fibres are tracked in DTI. The analogue to the diffusion tensor for image orientation is the structural tensor, which can provide better local orientation information than the gradient alone. I demonstrate this technique in portraits and general images, and discuss the parallels between fibre tracking and brush stroke placement, and frame it in the language of tractography. This work presents an exploratory investigation into the cross-domain application of diffusion tensor imaging techniques to painterly rendering of images. All the code is available at https://github.com/tito21/st-python
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