基于多尺度笔刷的分层油画风格化

基于图像的油画风格化绘制是计算机图形学领域非真实感绘制研究的热点之一.为了进一步提高图像油画风格化的质量,提出了一种基于多尺度笔刷的分层图像油画风格化绘制算法.该算法模拟艺术家的油画绘制过程,采用不同尺度的笔刷按照从粗到细的顺序逐层绘制.在每层笔刷绘制中,首先使用增量Voronoi序列采样点和图像切线方向场确定笔刷流线,然后结合笔刷形状与笔刷高度场进行纹理贴图,得到最终的图像油画风格化绘制结果.通过与现有算法比较,文中算法不仅能模拟真实的油画绘制过程,而且生成的油画效果层次感更强,充分体现了图像的结构特征和油画细节.     

一种基于笔刷的非真实感绘制算法的研究

非真实感绘制是计算机图形学的一个分支,基于笔刷的非真实感绘制是非真实感绘制的重要内容.给出一种基于笔刷的多重非真实感绘制算法.算法运用源图像灰度图像的颜色梯度法线方向作为笔刷的方向,以颜色空间距离来控制笔刷的绘制.实验结果表明,对于给定的输入图像,该算法能够有效地生成具有油画艺术风格的图像.     

基于计算机图像处理技术的油画生成系统设计

本文基于计算机图像处理技术设计了油画生成系统,通过接收并处理源图像,以栅格方式获取采样点,进行随机移动,通过水平向与垂直向边缘算子计算采样点位置梯度,以及窗口边缘强度,据此明确笔划方向与半径,以采样点于源图像的像素值为笔刷像素值,从而根据笔划方向、半径、笔刷像素值实现油画绘制.通过系统测试表明,油画生成系统绘制效果接近人工绘画效果,就笔触与颜色层面,都着重于突显油画绘制特色,高度符合人们对油画绘制的个性化与独特化需求;绘制手法与人工绘制手法相符,笔刷方向更具合理性,颜色进行了适度调整,使得整体更趋向于暖色系.     

基于随机笔刷的油画绘制方法

本文提出了一种基于随机笔剧的油画绘制方法。笔剧由一系列骨架点控制，使用随机数来定义每一组笔画的起始点，运用像素8-邻域的概念生成一系列骨架点。通过多个参数的不同设置，能够方便灵活地把一幅照片渲染成具有一定风格的艺术图像。     

粉笔画艺术风格模拟

目的 对不同艺术风格的模拟和绘制是非真实感绘制技术的主要任务之一,目前非真实感绘制技术已对油画、水彩画、中国书法等国内外艺术风格进行了模拟,然而对粉笔化艺术风格的模拟方法并不多见。本文提出了一种基于滤波扩散和线积分卷积（LIC）的粉笔画艺术风格绘制技术。方法 首先输入2维目标图像,通过对目标图像二值化处理、边缘提取操作,获得连续、光滑的边缘信息,并采用滤波扩散技术对边缘图像进行扩散处理,模拟粉笔画中笔划的毛糙效果,同时通过采用图像增强方法增强了笔划的细节信息;其次,由于真实粉笔画在创作时,粉笔颜料黏附在图像局部区域,形成具有方向的笔刷纹理效果,算法通过在目标图像中添加白噪声,基于线积分卷积LIC产生具有方向的粉笔画笔刷纹理,并通过形态学膨胀处理获得粉笔画的笔划纹理,模拟出粉笔画中笔划的笔触特征。再次,真实的粉笔画艺术效果往往在黑板、木材等材质中创作,算法将产生的笔刷纹理图像、色彩信息以及边缘图像通过图层映射方法,映射到黑板材质等输入背景图像中,产生最终的粉笔画艺术效果图像。结果 通过对输入2维图像进行实验,模拟出具有粉笔画艺术效果的结果图像,突出了粉笔画的线条细节信息和笔划艺术特征。结论 提出了一种粉笔画艺术效果模拟算法,非真实感绘制领域的有效补充,算法简单有效,能模拟出真实的粉笔画艺术效果,增强了艺术表现力。     

“笔”出高低“刷”出精彩——巧妙设置Photoshop中“笔刷”

Adobe公司的Photoshop图像处理软件可谓是“声名显赫”，目前全球有数以万计的Photoshop用户，其中的大部分用户主要用该软件处理图像，所以对“笔刷”的使用频率并不是很高，其实“笔刷”的奥妙更会让每个用户“欣喜不已”。[编者按]     

一种多笔触各向异性梵高风格油画的渲染方法

为使计算机模拟出更逼真的流体风格（涡旋状）梵高油画特效,本文提出了一种多尺度各向异性偏微分方程的模拟方法,该方法利用P M和J.Weickert加权模型在滤噪音时产生的虚假条纹来模拟梵高油画的涡旋状,并在油画绘制时给图像平坦区域加入高斯噪音和采用多尺度弧形笔刷的技术来处理整个渲染过程。实验结果表明：本方法除了很好的模拟了梵高油画的抽象效果外,还增加了油画的层次感和更多的涡旋状。     

实时绘制3D中国画效果

介绍了一个利用可编程图形硬件对3D山峦模型进行实时水墨效果绘制的系统．首先建立对应于不同层次的一组纹理图像，图像中的笔刷纹理按照手工绘画那样非均匀分布并构成明暗变化的纹理序列．系统在运行，首先检测山峦模型的位置来确定它所在的层，然后根据山峦模型的光照值对相应的纹理进行混合，山峦底部则与背景的云雾图进行混合，以得到最终的具有国画风格的画面．     

支持多风格的图像艺术化快速处理算法

为了使目标图像在用户的控制和编辑下快速学习参考图的颜色和笔刷特征,得到多个参考样图的艺术风格,提出一种支持多风格的图像艺术化快速处理算法.首先以目标图像流场为引导对非等轴纹理合成算法进行改进,学习得到样图的纹理和笔刷特征;然后通过设计和实现基于CUDA加速的并行合成算法,加快样图风格的学习速度;此外,为增强学习结果的可控性,设计完成了用户交互式的风格编辑接口,可根据用户指定的不同样图风格块对目标图像实现区域相关的多风格图像艺术化,提供所见即所得的交互设计体验.风格学习实例的结果表明,与已有的基于学习的风格化算法相比,该算法具有更快的合成速度和更强的灵活性.     

书法笔划

Dave Curd透露如何创建自定义笔刷并运用这些笔刷模仿书法笔划，快速创建花体字，之后还将向大家展示如何运用这些技巧在古典的希腊陶器脉络上绘制图片。[编者按]     

Video painting with space-time-varying style parameters

Artists use different means of stylization to control the focus on different objects in the scene. This allows them to portray complex meaning and achieve certain artistic effects. Most prior work on painterly rendering of videos, however, uses only a single painting style, with fixed global parameters, irrespective of objects and their layout in the images. This often leads to inadequate artistic control. Moreover, brush stroke orientation is typically assumed to follow an everywhere continuous directional field. In this paper, we propose a video painting system that accounts for the spatial support of objects in the images or videos, and uses this information to specify style parameters and stroke orientation for painterly rendering. Since objects occupy distinct image locations and move relatively smoothly from one video frame to another, our object-based painterly rendering approach is characterized by style parameters that coherently vary in space and time. Space-time-varying style parameters enable more artistic freedom, such as emphasis/de-emphasis, increase or decrease of contrast, exaggeration or abstraction of different objects in the scene in a temporally coherent fashion.     

Stylized rendering using samples of a painted image

We introduce a novel technique to generate painterly art map (PAM) for 3D non-photorealistic rendering. Our technique can automatically transfer brush stroke textures and color changes to 3D models from samples of a painted image. Therefore, the generation of stylized images/animation in the style of a given artwork can be achieved. This new approach works particularly well for a rich variety of brush strokes ranging from simple 1D and 2D line-art strokes to very complicated ones with significant variations in stroke characteristics. During the rendering/animation process, the coherence of brush stroke textures and color changes over 3D surfaces can be well maintained. With PAM, we can also easily generate the illusion of flow animation over a 3D surface to convey the shape of a model.     

Painterly rendering with content-dependent natural paint strokes

We present a new painterly rendering method that simulates artists’ content-dependent painting process and the natural variation of hand-painted strokes. First, a new stroke layout strategy is proposed to enhance the contrast between large and small paint strokes, which is an important characteristic of hand-painted paintings. Specifically, the input image is partitioned into nonuniform grids according to its importance map, and determined by the grid size, an individually constructed paint stroke is applied in each grid. Second, an anisotropic digital brush is designed to simulate a real paint brush. In particular, each bristle of the digital brush has an individual color, so that strokes rendered by the new brush can have multiple colors and naturally varied textures. Finally, we present a novel method to add lighting effects to the canvas. This lighting imitation method is robust and very easy to implement, and it can significantly improve the quality of rendering. Comparing with traditional painterly rendering approaches, the new method simulates more closely the real painting procedure, and our experimental results show that it can produce vivid paintings with fewer artifacts.     

Contour-driven Sumi-e rendering of real photos

We propose a sketch-based system for rendering oriental brush strokes on complex shapes. While previous research has focused on methods for converting user-specified trajectories into oriental ink painting (Sumi-e) strokes, we propose an approach that takes as input the contours of complex shapes, estimates automatically the sizes of the brush footprints, their orientations, and the brush trajectory. Then, it renders them into oriental ink paintings. The approach performs in three steps; first complex shapes are decomposed into elementary shapes that can be rendered with a single brush stroke. Second, the brush trajectory estimation is formulated as the minimization of an energy function that measures the quality of the trajectory. Finally, the estimated trajectories are rendered into brush strokes by mapping on them footprint textures scanned from real images. We demonstrate that the proposed contour-driven approach is particularly suitable for converting real images into Sumi-e paintings with minimum interaction. To this end we develop a system where images are either interactively or automatically segmented into elementary shapes and converted into Sumi-e paintings. For the automatic segmentation, we observe that the strokes in Sumi-e paintings are nearly parallel to the edge tangent flow (ETF). We propose a segmentation procedure that is based on clustering the curves obtained by integrating the ETF of real images. Experiments on complex shapes show that the proposed contour-based approach produces a large variety of complex strokes compared to trajectory-based approaches.     

一种实现森林场景漫游的几何与图像混合绘制方法*

提出了一种基于粒子系统和Particle System API的景物模拟方法,并采用Line方式取代传统的Point方式渲染粒子,结合纹理映射方法实现了多种喷泉模拟。实验证明用该方法模拟喷泉效果比较真实,速度快,在普通微机上可以得到令人满意的效果。     

虚拟场景中基于Motion Vector的人群图像绘制算法

针对虚拟场景中大规模人群图像绘制时由于采集图像数量有限,在视点连续变化时出现跳变的问题,提出了一种基于Motion Vector的图像绘制算法,该算法通过移动原始图像的像素位置生成相邻视点位置的图像,使得各个离散采样之间能够连续过渡,很好地解决了由于采样不足而引起的跳变问题,同时保证了绘制系统的实时性,实验结果验证了该算法的有效性。     

应用RBF神经网络的体绘制传递函数设计方法

提出一种基于RBF神经网络的体绘制多维传递函数设计方法,利用直观的交互界面,通过画笔获得感兴趣体素的特征信息作为训练样本对RBF神经网络进行训练,使用训练后的RBF神经网络实现全部体素的分类识别,对不同的分类结果赋予不同的光学参数进行显示,自动完成传递函数的设计。实验结果表明,所设计的交互界面能直观方便地定义感兴趣的对象,大幅提高人机交互的效率;RBF神经网络的自主学习能力能够避免传递函数设计的盲目性,增强感兴趣区域的绘制效果,实现传递函数设计的自动化和智能化。