Combining Sketch and Tone for Pencil Drawing Production paper reading (4)

本文探讨了基于色调分布图的手绘风格渲染技术,通过收集20种色调纹理并利用多层线条模拟人类反复绘制的过程,实现了局部色调的精细调整。文中详细介绍了如何通过模式图P和参数β来控制渲染效果,最终生成逼真的手绘风格图像,并进一步转化为彩色铅笔素描。

现在有了色调分布图,要为进行色调渲染,但是注意是没有明显的走向,仅包含色调信息的色调图。作者根据得到的色调分布图,进行探索,得到了人类手绘的色调模式图P,P随着β变化,即手绘的密度加深。
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作者从渲染的图例中收集了20种色调纹理,一张图片只需要一种色调纹理就可以,人类画图的时候,对某个地方的色调是通过反复画的方式得到的,作者使用了多层线条模拟这个过程,假设局部色调为J,而模式图为H,反复画就是用了一个指数的过程,对H进行β次方,得到了J,另外β必须满足局部平滑,即:
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得到最终纹理图:
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将得到了线条S和色调T按像素相乘,得到了R

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后面还提及彩色铅笔素描,是在得到了原始素描R的基础上进行颜色空间的映射得到的。

Robust controller design involves the synthesis of a controller that can handle uncertainties and disturbances in a system. This is typically done by formulating the problem as an optimization problem, where the goal is to find a controller that minimizes a cost function subject to constraints. One approach to robust controller design involves combining prior knowledge with data. Prior knowledge can come from physical laws, engineering principles, or expert knowledge, and can help to constrain the search space for the controller design. Data, on the other hand, can provide information about the behavior of the system under different conditions, and can be used to refine the controller design. The combination of prior knowledge and data can be done in a number of ways, depending on the specific problem and the available information. One common approach is to use a model-based design approach, where a mathematical model of the system is used to design the controller. The model can be based on physical laws, or it can be derived from data using techniques such as system identification. Once a model is available, prior knowledge can be incorporated into the controller design by specifying constraints on the controller parameters or the closed-loop system response. For example, if it is known that the system has a certain level of damping, this can be used to constrain the controller design to ensure that the closed-loop system response satisfies this requirement. Data can be used to refine the controller design by providing information about the uncertainties and disturbances that the system is likely to encounter. This can be done by incorporating data-driven models, such as neural networks or fuzzy logic systems, into the controller design. These models can be trained on data to capture the nonlinearities and uncertainties in the system, and can be used to generate control signals that are robust to these uncertainties. Overall, combining prior knowledge and data is a powerful approach to robust controller design, as it allows the designer to leverage both physical principles and empirical data to design a controller that is robust to uncertainties and disturbances.
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