From image parsing to painterly rendering
From image parsing to painterly rendering
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DOI：
10.1145/1640443.1640445
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
K. Zeng;Mingtian Zhao;Caiming Xiong;Song-Chun Zhu
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文献类型：
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作者：
K. Zeng;Mingtian Zhao;Caiming Xiong;Song-Chun Zhu
我们提出了一个语义驱动的方法，基于笔画的绘画渲染，最近的图像解析技术的基础上[涂等。2005年;涂和朱2006年]在计算机视觉。图像解析集成了区域分割、曲线绘制和对象类别识别。在一个交互式的方式，我们分解成一个层次结构的组成部分，在解析树表示与树中的节点之间的遮挡关系的输入图像。为了绘制图像，我们构建了一个画笔字典，其中包含从专业艺术家那里收集的四种形状/外观类别的画笔示例的大集合（760），然后我们从字典中选择适当的画笔，并将它们放置在画布上，由解析树中包含的图像语义引导，每个图像组件和层以各种风格绘制。在此过程中，场景和对象类别还决定了图像细节的非均匀合成的颜色混合和阴影策略。与以往的方法相比，该方法具有更丰富的图像语义信息，能够更好地模拟画家使用高质量画笔字典的绘画技巧。我们已经测试了我们的方法对大量（数百）的图像，它产生了令人满意的绘画效果。
We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.