通道分解为绘画动作

摘要：

这项工作提出了一种将深度神经网络的卷积层分解为绘画动作的方法。为了表现得像人类画家一样，这些动作由模拟手部运动、绘画颜色变化、笔划形状和笔划风格的成本驱动。为了帮助规划，应用Mask R-CNN来检测对象区域并决定绘制顺序。所提出的绘画系统引入了各种扩展的艺术风格，根据所选择的参数。进一步的实验进行评估的通道渗透和通道的敏感性上的笔划。

英文摘要：

This work presents a method to decompose a convolutional layer of the deep neural network into painting actions. To behave like the human painter, these actions are driven by the cost simulating the hand movement, the paint color change, the stroke shape and the stroking style. To help planning, the Mask R-CNN is applied to detect the object areas and decide the painting order. The proposed painting system introduces a variety of extensions in artistic styles, based on the chosen parameters. Further experiments are performed to evaluate the channel penetration and the channel sensitivity on the strokes.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。