CCC——计算机中国书法系统

计算机汉字书法系统(CCC)是模拟手写汉字书法的过程,在屏幕上显示或在纸上打印出汉字。基本思想是,用程序过程模拟毛笔的笔划,用笔径函数过程和笔深函数过程控制笔划,从而产生各种风格的笔体。再用组字规则知识库组成汉字。利用组字规则的变异和笔划的变异可以模拟出不同人的笔体。     

一个联机书法临摹系统的设计与实现

提出一个联机书法临摹系统,对传统的中国书法学习过程进行计算机模拟.系统基于Wintab标准捕获书写实时信息,并以一维基元序列模型表示和存储书法范字;学习者在线临摹时,系统基于椭圆模型和动态信息进行书法范字绘制,模拟范字创作时的书写笔顺、运笔方法、提按轻重、行笔节奏等体现书法美学内涵的动态过程,再现结果如同老师身临示范.系统可作为中国书法教育辅助软件或自学工具.     

书法机器人研究综述

书法机器人将书法创作与机器人技术相融合,通过控制机械臂模拟人的书写行为和书法创作,再现艺术魅力.机器人书法作为机器人运动规划的典型应用,对传统书法文化的传承和教育具有深远意义.按照书法机器人的研究脉络,回顾分析书法机器人的发展历程.首先介绍笔画分离与提取关键技术,总结虚拟笔刷建模的两种经典笔触模型;然后详细阐述书写轨迹涉及到的核心技术,应用不同的书写轨迹规划控制书写路径,在此基础上分析目前书写评价指标,讨论书法机器人的评价模型;最后回顾总结已有的研究进展及流程,对比传统书法机器人和智能书法机器人各自的优缺点,提出将传统的书写轨迹路径、笔触模型融入到智能书法机器人训练模块中,以弥补其在笔画书写顺序方面的不足.同时,展望书法机器人未来的研究方向和发展趋势,指出未来书法机器人可围绕深度神经网络、深度神经进化、深度强化学习等开展研究.     

基于笔纸墨物理属性的毛笔书法仿真

为了更真实地模拟毛笔书法独特的艺术效果,提出一种基于笔纸墨物理属性的毛笔书法仿真方法.通过分析笔纸墨在书写过程中的相互作用,提取笔纸墨中与书法效果表达相关的物理属性,建立简化的数学模型,提出墨水十字扩散算法;利用F-Tablet采集的手写信息生成仿真过程中的参数,省去了烦琐的参数设置过程.实验结果表明,该方法在计算机中再现了人们毛笔书法的创作过程,可以达到逼真的毛笔书法仿真效果.     

基于审美特征的书法评价模型构造与实现

传统的书法学习,需要教师要对学生习作进行分析和点评,一旦离开教师的指导,提高就变得十分困难.基于此,提出一个基于审美特征的书法评价模型,实现对传统书法教学过程的计算机模拟.借助该系统,教师可以录入书法范字;学生可以运用手写设备进行在线临摹练习.练习结束后,系统能够给出学生习作在结构与笔法两方面详细的指导意见.实验表明,模型为书法学习提供了有效帮助.     

一种融合区域笔划和梯度笔划的油画生成方法

基于笔划的油画生成是油画生成中最为重要的技术,它模拟真实画家使用 笔划的创作过程,以达到逼真的手绘作品的效果。分析了基于图像分割的区域笔划生成油画 的优缺点,提出一种融合区域笔划和梯度笔划的油画生成方法,首先通过图像分割生成区域 笔划,然后根据图像分割水平确定梯度阈值筛选出梯度笔划。通过实验可以看出,该方法生 成速度较快,不仅使笔划更加灵活多变,而且可以弥补区域笔划生成所产生的缝隙。     

关于《人工神经元网络及其在知识系统中的应用》文集简介

1987年秋,中科院自动化所接受了863智能计算机专家组委托的课题“人工智能基础”,他们在原有工作基础上着重对知识系统进行了研究.从有关人的思维的角度考虑,两种重要的思维方式是逻辑思维和形象思维(直感思维),以往以规则为基础的知识系统可以认为是模拟人的逻辑思维,而模拟人的形象思维的工作还很少.他们从网络结构模型出发来探讨形象思维,对已有的一些人工神经元网络模型加     

三维虚拟环境中的书法创作

为了充分利用数字化技术促进书法艺术的教学和发展,提出并实现了虚拟化书法创作系统.首先构建基于三角网格的毛笔虚拟模型;然后通过常用曲线拟合笔画书写路径,并在笔画路径上指定关键帧,以表现书写时毛笔在相应位置上的节奏快慢和提、按、顿、挫等状态;最后实时绘制书法作品并渲染书写过程.用户可以随意用文中系统进行书法创作,在三维环境中逼真地展示书法创作的全过程.实验结果表明,所构建的毛笔虚拟模型计算量小、实时性强,创作效果与真实毛笔的书写效果非常接近.     

基于统计模型的书法笔画轮廓恢复*

获取书法汉字笔画的完整轮廓是进行计算机书法模拟和创作的重要前提,而笔画间的相互覆盖使得获取的笔画轮廓不完整。结合一个已有的统计模型,提出了一种新的笔画轮廓恢复方法。该方法通过建立轮廓模型、统计学习相似笔画来实现有缺失笔画轮廓的恢复。其主要过程包括笔画轮廓建模、建立并训练统计模型、运用统计模型恢复轮廓。实验表明该方法既有广泛的适应性又呈现良好的效果。     

非线性系统辨识

激光焊接过程是典型的具有噪声和扰动影响的非线性系统.利用Hammerstein模型的线性和非线性分离的特性可以建立起关于激光焊接过程的非线性模型,并以此为基础得到非线性系统参数的无偏估计,经阶跃响应验证,该模型有效地模拟了激光焊接过程.     

基于图像的非参数化采样印章生成

通过对一系列同一风格的样本印章的非参数化采样，由输入目标篆文自动生成相应的该风格印章．以子分笔画为基本单位，采用K近邻法使样本风格自然融入生成印章中．各子分笔画通过变半径圆形brush沿非均匀B样条曲线扫掠来拟合．提出了带约束逐层细分非均匀采样法来得到各插值型值点．文中算法简单明了，几何意义明显．实验结果证明可以生成逼真的指定风格的新作品．     

Chinese calligraphic style representation for recognition

Chinese calligraphy draws a lot of attention for its beauty and elegance. The various styles of calligraphic characters make calligraphy even more charming. But it is not always easy to recognize the calligraphic style correctly, especially for beginners. In this paper, an automatic character styles representation for recognition method is proposed. Three kinds of features are extracted to represent the calligraphic characters. Two of them are typical hand-designed features: the global feature, GIST and the local feature, scale invariant feature transform. The left one is deep feature which is extracted by a deep convolutional neural network (CNN). The state-of-the-art classifier modified quadratic discriminant function was employed to perform recognition. We evaluated our method on two calligraphic character datasets, the unconstraint real-world calligraphic character dataset (CCD) and SCL (the standard calligraphic character library). And we also compare MQDF with other two classifiers, support vector machine and neural network, to perform recognition. In our experiments, all three kinds of feature are evaluated with all three classifiers, respectively, finding that deep feature is the best feature for calligraphic style recognition. We also fine-tune the deep CNN (alex-net) in Krizhevsky et al. (Advances in Neural Information Processing Systems, pp. 1097–1105, 2012) to perform calligraphic style recognition. It turns out our method achieves about equal accuracy comparing with the fine-tuned alex-net but with much less training time. Furthermore, the algorithm style discrimination evaluation is developed to evaluate the discriminative style quantitatively.     

根据形状相似性的书法内容检索

提出了一种根据形状相似性来检索书法字的算法：将扫描的书法作品先切分成单个书法字,并提取轮廓特征;接着由形状相似性算法计算相似度,然后按相似程度大小显示同一字不同的书法风格.实验证明,该算法具有很好的效果.     

基于信息熵的风格绘画分类研究

针对艺术风格绘画分类算法中存在的精度和效率不高等问题,提出一种基于信息 熵的艺术风格绘画分类算法。首先选取西方漫画、素描、油画、水彩画,以及国内烙画、水墨 画、壁画具有代表性的 7 种艺术绘画风格作为研究对象,对图像进行去噪、归一化等预处理。 其次,提取绘画艺术作品风格特征,分别求取图像的颜色熵、分块熵、轮廓熵,并合并构成不 同绘画风格的信息熵。信息熵求取时,将色彩空间转换为 Lab 颜色空间,通过 a、b 通道颜色值 及加权函数获得图像的颜色熵;通过对艺术图像分块求取信息熵,求取分块的信息熵均值获得 分块熵;通过 Contourlet 变换,求取艺术图像的轮廓信息,获得轮廓熵。接着,合并提取的颜 色熵、分块熵、轮廓熵,利用支持向量机(SVM)对艺术风格图像学习训练,获得艺术绘画风格 的分类模型;最后,提取待识别绘画风格样本的熵特征,通过 SVM 分类识别获得最终的分类 结果。该方法具有特征维数少、运算速度快、尺度不变性等优势,实验结果表明,其能提高不 同绘画风格的分类精度和效率。     

Trajectory generation of the writing–brush for a robot arm to inherit block–style Chinese character calligraphy techniques

This paper relates the trajectory generation of the writing-brush for a robot arm to inherit Chinese character calligraphy (CCC) techniques. First, to preserve the characters written by famous calligraphers in Chinese history, we constructed a CCC database which contains 29 456 characters written by different calligraphers in different styles (ancient, angular, block, semi-cursive and cursive style). With this CCC database, it is possible to search, restore and append the calligraphy writing for a specific character. Second, we mainly relate the inheritance of CCC. Because CCC is not a static thing, but a dynamic process of an activity which concerns a lot of complicated factors such as the pressure and speed control of the writing brush, how to write the start and end of strokes, etc., we propose to inherit this dynamic process by a robot. This paper is limited to discussing how to inherit the writing techniques for block-style calligraphy writing by a robot arm. The total number of Chinese characters is more than 800 000. These characters can be constructed by basic strokes. There are 28 different kinds of strokes used to construct all of these characters. The skeleton of a stroke is decided by the control points that exist within the stroke. The shape of a stroke is determined by the trajectory derived from the control points and the pressure to the writing brush. The control points for 28 strokes are given and the control techniques for a robot to write these strokes in block style are developed. At present, the robot can write any character in block style.     

Stroke Style Analysis for Painterly Rendering

We propose a novel method that automatically analyzes stroke-related artistic styles of paintings.A set of adaptive interfaces are also developed to connect the style analysis with existing painterly rendering systems, so that the specific artistic style of a template painting can be effectively transferred to the input photo with minimal effort.Different from conventional texture-synthesis based rendering techniques that focus mainly on texture features, this work extracts, analyzes and simulates high-level style features expressed by artists’ brush stroke techniques.Through experiments, user studies and comparisons with ground truth, we demonstrate that the proposed style-orientated painting framework can significantly reduce tedious parameter adjustment, and it allows amateur users to efficiently create desired artistic styles simply by specifying a template painting.     

启体书法字的矢量化

矢量字以其可以方便地进行各种变换和高质量的显示及输出效果得到广泛应用,汉字的矢量化是中文信息处理的重要内容之一。该文通过非细化的矢量化算法中的轮廓线的方法,针对启功书法字中的3 755个一级汉字完成了矢量化,并建立了包括每个字的笔画的矢量表示、笔画顺序等信息的启功书法的矢量字库,为启体书法的逐笔临摹学习奠定了基础。文章详细论述了对书法字轮廓的提取过程,笔画分割过程和对提取结果的优化处理方法。     

计算机辅助书法牌匾设计

通过融合书法字形状、方向和上下文特征并结合反馈技术,实现一种计算机辅助书法牌匾设计的方法．首先提取书法字特征表达书法字风格,然后通过风格一致性模型得到牌匾候选集,最后用对等反馈传递调整书法字的风格相似度．通过实验测试了不同的融合方法和反馈机制对牌匾生成的影响,结果表明融合和反馈均能提高牌匾的生成效果．     

Automatic generation of artistic chinese calligraphy

Chinese calligraphy is among the finest and most important of all Chinese art forms and an inseparable part of Chinese history. Its delicate aesthetic effects are generally considered to be unique among all calligraphic arts. Its subtle power is integral to traditional Chinese painting. A novel intelligent system uses a constraint-based analogous-reasoning process to automatically generate original Chinese calligraphy that meets visually aesthetic requirements. We propose an intelligent system that can automatically create novel, aesthetically appealing Chinese calligraphy from a few training examples of existing calligraphic styles. To demonstrate the proposed methodology's feasibility, we have implemented a prototype system that automatically generates new Chinese calligraphic art from a small training set.