基于小波变换和奇异值分解的剪纸纹样识别

针对民间传统剪纸艺术的计算机创作问题,在分析剪纸艺术特点的基础上,提出一种基于小波变换和奇异值分解的剪纸纹样识别方法.首先对剪纸纹样图像进行归一化和二值化处理,然后应用小波变换提取剪纸纹样图像的低频分量并进行奇异值分解,最后通过对奇异值进行归一化和降维处理作为最终的特征向量,利用最近邻分类器进行模式识别.实验结果表明,该方法能够有效地去除噪声干扰,较好的识别有一定艺术夸张变形的剪纸纹样.     

基于多分辨Fourier-Mellin变换的剪纸纹样识别算法

提出一种基于多分辨Fourier-Mellin的剪纸纹样识别算法。该算法先对剪纸纹样图像进行Fourier-Mellin变换,再对变换后的图像通过小波变换计算出各层方差和均值,得到剪纸纹样不同子带的特征值,应用支持向量机对剪纸纹样进行识别。实验证明,该方法不仅具有平移、旋转和尺度不变性,而且适用于有夸张艺术变形的剪纸纹样识别。     

基于不变矩和BP神经网络的剪纸纹样识别

针对剪纸纹样艺术夸张变形的特点,将剪纸图像进行预处理,提取7个不变矩作为剪纸纹样的特征向量,采用LM算法优化BP神经网络,通过归一化后的不变矩对BP神经网络进行训练,应用训练后的神经网络作为分类器对剪纸纹样进行模式识别,实验证明该方法能够较好地识别有一定艺术变形的剪纸纹样。     

基于装饰纹样的剪纸图案设计方法

以传统剪纸图案为研究对象,分解出构成剪纸图案的纹样,将这些纹样分为独立纹样和复合纹样。通过对二维图形进行布尔运算等方法构建丰富的独立纹样库。以独立纹样为基础,应用扩大对称、渐变等图案设计技术建立复合纹样库,同时,对折叠剪纸效果进行模拟。实验结果表明,该方法能够根据需要生成不同形式的折叠剪纸图案。     

基于Zadeh推理的故障空调噪音识别

通过声音来实现故障诊断在工业领域应用比较广泛。本文的主要目标是给出故障空调在运行状态下的声音,使用Zadeh推理的方法对故障空调的声音进行识别,进而推断出空调内部具体哪个部件或零件发生故障。本文首先对声音进行快速傅里叶变换,根据三分之一倍频程对整个频率进行划分,并对生成的频率进行标准化处理;当空调内部的零部件发生损坏时,噪音峰值就会在某一段或几段频率中有升高,从而提取每个频程的峰值作为特征向量。通过“专家经验”得出一个逻辑关系R矩阵,使用matlab软件将特征向量与R矩阵进行点积运算得到故障诊断的结果,最后GUI界面呈现出结果。实验表明,本方法对空调噪音的识别诊断效果比较理想。最后给出本方法适用的一些条件。本方法的实现原理简单,在空调噪音故障诊断应用领域可以有一定的应用空间。     

基于遗传算法的剪纸图案创新设计

通过对剪纸造型的分析和概括,抽象出所需的纹样,利用遗传算法来生成纹样,并且在适应度函数的构建方面,增加了对曲线均匀度和平滑度的度量,以此构造丰富多彩的纹样库。然后通过对剪纸造型结构上的分析,对纹样库中的各种纹样进行自动选取及组装,产生各种各样风格不同的剪纸图案造型,并可根据实际需要进行人工修改。实验证明,该方法能够增强剪纸图案设计的创新性。     

基于Contourlet变换和支持向量机的纹理识别方法

针对变换域中图像纹理识别时如何选择最佳特征向量的问题,利用Contourlet变换的多方向、多尺度选择性和各向异性,将图像从空间域变换到频率域,全面地提取了Contourlet变换分解后低频子带、中频子带和高频子带的特征,输入支持向量机(SVM)分类器进行分类识别。利用Brodatz纹理库进行仿真实验,实验结果表明低频均值方差和高频能量作为组合特征时识别准确率可达98.75%,且特征向量维数少,是在Contourlet变换下表示图像纹理的最优特征。     

基于小波变换和分形维数的车牌汉字识别

提出一种基于小波变换与分形维数的车牌汉字识别方法.对字符图像进行预处理和小波变换,应用改进的微分盒维法计算图像分形盒维值,并构造特征向量,利用支持向量机分类器对字符进行分类与识别.实验结果表明,该方法对模糊字符的识别具有鲁棒性,可提高汉字识别率.     

基于第二代Bandelet的脱线签名识别

由于脱线签名鉴定丢失了在书写过程中的动态信息,鉴定难度大.本文针对脱线签名识别的特点,提出了基于Ban-delet变换的特征提取方式,将传统的结构特征和统计特征有效地结合起来.通过K-L变换降低特征向量的维数,然后采用支持向量机(SVM)的方法进行训练和识别.对400个手写样本进行了识别,实验证明该方法能有效提高脱线签名的识别率.     

剪纸图案的构造模式分析和数字化建模

为了更好地对剪纸图案进行编辑、重用和个性化设计,提出一种剪纸图案的构造模式分析和数字化建模方法.首先对剪纸图像进行矢量化,获得各个独立形状的剪纸图案;然后采用改进的形状上下文方法对它们进行相似性聚类;最后检测和识别相似图案的空间分布模式,将输入剪纸图像转化为一个由元素层、相似层、纹样层、根结点组成的树状层次结构.文中提出的建模方法凸显了剪纸图案重复有序和模式化的构造特点,能够支持快速的几何连通性判断.实验结果表明,该方法能够大大降低个性化剪纸设计的难度,提高剪纸图案的编辑效率.     

染色剪纸效果仿真方法研究

通过分析手工染色剪纸的特点,提出一种染色剪纸效果仿真方法。该方法借鉴粒子系统基本概念,应用纹理映射技术建立染色画笔模型,对剪纸图案进行染色,画笔的起落及走向分别由鼠标按键和移动方向控制,通过设置画笔参数,可以得到不同的染色效果。应用基于纹样的剪纸图案设计方法为染色剪纸添加丰富的纹样。最后将纸纹理的干扰作用考虑进去,得到更加逼真的仿真结果。实验结果表明,该方法较成功地模拟了染色剪纸效果。     

A hierarchical abnormal human activity recognition system based on R-transform and kernel discriminant analysis for elderly health care

A hierarchical human activity recognition (HAR) system is proposed to recognize abnormal activities from the daily life activities of elderly people living alone. The system is structured to have two-levels of feature extraction and activity recognition. The first level consists of R-transform, kernel discriminant analysis (KDA), $k$ -means algorithm and HMM to recognize the video activity. The second level consists of KDA, $k$ -means algorithm and HMM, and is selectively applied to the recognized activities from the first level when it belongs to the specified group. The proposed hierarchical approach is useful in increasing the recognition rate for the highly similar activities. System performance is analyzed by selecting the optimized number of features, number of HMM states and the number of frames per second to achieve maximum recognition rate. The system is validated by a novel set of six abnormal activities; falling backward, falling forward, chest pain, headache, vomiting, and fainting and a normal activity walking. Experimental results show an average recognition rate of 97.1 % for all the activities by using the proposed hierarchical HAR system.     

剪纸艺术元素在现代包装设计运用中的“情感”探究

民间剪纸是中国古老的传统民间艺术,作为一种装饰符号,它具有独特的东方韵味。文章通过分析剪纸艺术元素在现代包装设计中的运用,指出二者的结合不仅仅只是体现一种传统文化内涵,而更应该使新形势下的包装设计呈现出情感化的趋势,包装设计应该更新设计理念、创新设计技法,从而呈现出以人为本的设计,最大限度地满足消费者在产品包装中的情感需求。     

面向三维剪纸的网格模型切割方法

提出了一种改进的三维网格模型切割算法,在切割每个图案时首先检查面片区域是否已被其他图案修改,并在修改之后对应的面片区域上进行切割且记录切割结果,最后将三维网格模型表面封闭剪纸图案内的顶点和面片剔除,得到像手工剪纸那样的镂空效果.三维剪纸可以广泛应用于动画、教育以及娱乐产业中.     

Deterministic Learning and Rapid Dynamical Pattern Recognition

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns. First, it is shown that a time-varying dynamical pattern can be effectively represented in a time-invariant and spatially distributed manner through deterministic learning. Second, a definition for characterizing similarity of dynamical patterns is given based on system dynamics inherently within dynamical patterns. Third, a mechanism for rapid recognition of dynamical patterns is presented, by which a test dynamical pattern is recognized as similar to a training dynamical pattern if state synchronization is achieved according to a kind of internal and dynamical matching on system dynamics. The synchronization errors can be taken as the measure of similarity between the test and training patterns. The significance of the paper is that a completely dynamical approach is proposed, in which the problem of dynamical pattern recognition is turned into the stability and convergence of a recognition error system. Simulation studies are included to demonstrate the effectiveness of the proposed approach     

Conception of complex probabilistic neural network system for classification of partial discharge patterns using multifarious inputs

Pattern recognition has a long history within electrical engineering but has recently become much more widespread as the automated capture of signal and images has been cheaper. Very many of the application of neural networks are to classification, and so are within the field of pattern recognition and classification. In this paper, we explore how probabilistic neural networks fit into the earlier framework of pattern recognition of partial discharge patterns since the PD patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. Also this paper describes a method for the automated recognition of PRPD patterns using a novel complex probabilistic neural network system for the actual classification task. The efficacy of composite neural network developed using probabilistic neural network is examined.     

一种支持纸的折叠、剪切与展开的计算模型

为了满足儿童剪纸方案的交互设计以及向儿童展示典型的儿童剪纸过程的需要,设计了一个支持纸的折叠、剪切与展开的计算模型,它包括当前纸态的几何与拓扑数据结构、操作过程的记录方法以及各相关功能的实现算法。以此计算模型为核心所开发的原型软件(命名为Virtual Paper)验证了该模型的可行性。     

A kernel autoassociator approach to pattern classification.

Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.     

Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.