基于Gabor、Fisher脸多特征提取及集成SVM的人脸表情识别*

针对于静态的灰度图像表情库,提出了基于多种脸部表情特征多级分类的表情识别算法。首先在选取的人脸特征点上做局部的Gabor小波变换,为了提高特征提取速度,利用改进的弹性图匹配算法来提取图像中的人脸有效区域,在提取的人脸区域中提取几何特征,并通过Fisher脸法提取统计特征,利用几何特征与建立的相应一级集成SVM来进行初次分类,最后利用Fisher特征与建立的相应二级集成SVM进行最终分类。通过在JAFFE与Cohn-Kanade表情库中实验,证明本文方法同单个特征相比较,具有更高的表情识别率以及更强的鲁棒性     

基于支持向量机的中国地鼠分类特征基因选取

针对中国地鼠基因表达谱数据维数高和样本小的特点,提出一种基于支持向量机(SVM)的分类特征基因选取方法。该方法利用改进的Fisher判别(FDR)基因特征计分准则剔除分类无关基因,提出由空间距离和功能距离组成的新距离作为相似性度量的标准进行冗余基因的剔除,采用SVM作为分类器检验特征基因的分类性能。实验结果表明,该方法有效地剔除了分类无关基因和冗余基因,选取的特征基因满足对中国地鼠正确分类的最小基因数。     

结肠癌基因表达谱的分类检测问题研究

随着大规模基因表达谱技术的发展,基于基因表达谱的癌症诊断方法正在成为临床医学上一种快速有效的诊断方法,但是由于基因表达数据维数过高、样本量小、噪声大,使得正确提取有关癌症的特征基因成为关键。以结肠癌肿瘤的基因表达谱数据为例,提出了结合Fisher权函数、离散傅里叶变换和主成分分析的混合特征基因提取方法,以多元Logistic回归分析和贝叶斯决策作为分类器进行肿瘤分类检测。实验结果表明,该方法对于结肠癌数据集CV识别准确率高达96.80%。     

Application of the Self-Organizing Feature Map Algorithm in Facial Image Morphing

A new facial image morphing algorithm based on the Kohonen self-organizing feature map (SOM) algorithm is proposed to generate a smooth 2D transformation that reflects anchor point correspondences. Using only a 2D face image and a small number of anchor points, we show that the proposed morphing algorithm provides a powerful mechanism for processing facial expressions.     

Strategies for Expressing Concise, Helpful Answers

An intelligent help system needs to take into account the user's knowledgewhen formulating answers. This allows the system to provide more conciseanswers, because it can avoid telling users things that they already know.Since these concise answers concentrate exclusively on pertinent newinformation, they are also easier to understand. Information about theuser's knowledge also allows the system to take advantage of the user'sprior knowledge in formulating explanations. The system can provide betteranswers by referring to the user's prior knowledge in the explanation(e.g., through use of similes). This process of refining answers is calledanswer expression.The process of answer expression has been implemented in the UCExpresscomponent of UC (UNIX Consultant), a natural language system that helps theuser solve problems in using the UNIX operating system. UCExpress separatesanswer expression into two phases: pruning and formatting.In the pruning phase, subconcepts of the answer are pruned by being markedas already known by the user (and hence do not need to be generated), ormarked as candidates for generating anaphora or ellipsis (since they arepart of the conversational context). In the formatting phase, UCExpressuses information about the user's prior domain knowledge to select amongspecialized expository formats,such as similes and examples, for expressing information to the user. Theseformats allow UCExpress to present different types of information to theuser in a clear, concise manner. The result of UCExpress' answer expressionprocess is an internal form that a tactical level generator can easily useto produce good English.     

谈高校艺术设计专业的色彩教学改革

如何探索一套适用于艺术设计专业特点的基础教学方法,利用有限的时间,通过有效的训练,为专业设计建立联系,打下基础,一直是艺术设计教育者应该思考和探索的问题。     

艺与道合

艺术是思想的产物,是艺术家素质修养的自然流露。艺术的表现需要出色的表现技法和丰富的精神内涵,正所谓艺与道合才能创作出优秀的艺术作品。中国的艺术是建立在中国传统哲学的基础上,当代艺术家们需要汲取中国传统哲学的精髓,表现语言、形式上应当在积极借鉴国内外优秀艺术作品的基础上博古通今,消化融合才能真正达到艺与道合,天人合一的境界。     

从意象思维到图式表达——论艺术设计意图的清晰化

设计活动是一个由模糊到清晰的过程,它是建立在人对事物的观察、感知与思考的基础上。在这个过程中思维意象要不断地呈现出来,呈现的方式有两种,一种是以图的形式,一种是以实物或模型的形式。图式表达相对来说是一种便捷、基础的方式。从这个角度来看,图像化的思维与表达对于研究形态的创造与变化来说是不可或缺的。本论文通过对意象思维的分析,针对形态创造的问题,提出一系列观察思考与表达的方法,以发现新的设计灵感源泉。     

论现代设计中的传统意境表达

本文着重阐述了"意境"的内涵、传统"意境"表达的重要性以及如何在现代设计中表达传统"意境",并初步探索了具体设计手法的可行性。     

Spatial interest pixels (SIPs): useful low-level features of visual media data

Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.