基于径向曲线与支持向量回归的颅骨修复方法

颅骨修复技术是对有缺损的颅骨补全对应的缺损部分,进而实现颅骨形状的完整性。针对高维颅骨数据,采用径向曲线来表示颅骨几何特征,结合最小二乘支持向量回归的方法构建颅骨修复模型。提取完整的三维颅骨模型的径向曲线,将其分为已有径向曲线和缺失径向曲线两部分作为训练样本,采用最小二乘支持向量回归统计模型复原出待修复颅骨的缺失径向曲线,进而合并生成待修复颅骨的完整径向曲线,通过迭代最近点算法将合并的颅骨径向曲线与颅骨统计模型进行匹配生成完整的三维颅骨模型。实验结果表明,该方法的平均误差达到6.834×10^-3,比主成分分析方法降低2.90倍,具有更好的修复效果。     

Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition

Recognizing human faces in various lighting conditions is quite a difficult problem. The problem becomes more difficult when face images are taken in extremely high dynamic range scenes. Most of the automatic face recognition systems assume that images are taken under well-controlled illumination. The face segmentation as well as recognition becomes much simpler under such a constrained condition. However, illumination control is not feasible when a surveillance system is installed in any location at will. Without compensating for uneven illumination, it is impossible to get a satisfactory recognition rate. In this paper, we propose an integrated system that first compensates uneven illumination through local contrast enhancement. Then the enhanced images are fed into a robust face recognition system which adaptively selects the most important features among all candidate features and performs classification by support vector machines (SVMs). The dimension of feature space as well as the selected types of features is customized for each hyperplane. Three face image databases, namely Yale, Yale Group B, and Extended Yale Group B, are used to evaluate performance. The experimental result shows that the proposed recognition system give superior results compared to recently published literatures.     

On minimum class locality preserving variance support vector machine

In this paper, a so-called minimum class locality preserving variance support machine (MCLPV_SVM) algorithm is presented by introducing the basic idea of the locality preserving projections (LPP), which can be seen as a modified class of support machine (SVM) and/or minimum class variance support machine (MCVSVM). MCLPV_SVM, in contrast to SVM and MCVSVM, takes the intrinsic manifold structure of the data space into full consideration and inherits the characteristics of SVM and MCVSVM. We discuss in the paper the linear case, the small sample size case and the nonlinear case of the MCLPV_SVM. Similar to MCVSVM, the MCLPV_SVM optimization problem in the small sample size case is solved by using dimensionality reduction through principal component analysis (PCA) and one in the nonlinear case is transformed into an equivalent linear MCLPV_SVM problem under kernel PCA (KPCA). Experimental results on real datasets indicate the effectiveness of the MCLPV_SVM by comparing it with SVM and MCVSVM.     

Summary queries for frequent itemsets mining

There are many advanced techniques that can efficiently mine frequent itemsets using a minimum-support. However, the question that remains unanswered is whether the minimum-support can really help decision makers to make decisions. In this paper, we study four summary queries for frequent itemsets mining, namely, (1) finding a support-average of itemsets, (2) finding a support-quantile of itemsets, (3) finding the number of itemsets that greater/less than the support-average, i.e., an approximated distribution of itemsets, and (4) finding the relative frequency of an itemset (compared its frequency with that of other itemsets in the same dataset). With these queries, a decision maker will know whether an itemset in question is greater/less than the support-quantile; the distribution of itemsets; and the frequentness of an itemset. Processing these summary queries is challenging, because the minimum-support constraint cannot be used to prune infrequent itemsets. In this paper, we propose several simple yet effective approximation solutions. We conduct extensive experiments for evaluating our strategy, and illustrate that the proposed approaches can well model and capture the statistical parameters (summary queries) of itemsets in a database.     

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多数现有特征提取方法仅采用简单的形态特征,存在走与跑识别率较低的问题。将运动速度特征与较精确分割并归一化图像大小后的主分量分析外形特征相结合,采用支持向量机从8个方向对跑、蹲、站、弯腰、招手、指和走7种人体行为进行识别,结果证明走与跑的识别率得到很大提高。     

支持向量机在文本特征提取中的应用研究

在文本自动分类中,针对如何进行文本特征的选择和提取这一关键和基础性工作,提出用支持向量度量词汇对分类的贡献,然后进行文本特征的提取。实验结果表明,该方法可以在确保分类信息不损失的前提下,降低向量空间的维数,提高分类器效率和分类准确率。     

支持向量预选的凸壳顶点法

为减少参训样本数量,加快支持向量机在大规模数据集上的学习速度,提出一种基于凸壳顶点法的支持向量预选算法.该算法基于线性可分样本集凸壳顶点的集合必然是支持向量超集的事实,运用对偶原理将凸壳顶点的求解转化为判断线性规划是否有解,从而求出样本集的凸壳顶点.构造了非线性映射函数,并将该算法推广到非线性可分样本集.基于人工数据集和标准数据集的实验结果验证了算法的有效性.     

辩论支持系统的设计与实现

本文阐述了辩论支持系统开发的背景和意义,介绍了基于J2EE的系统需求,分析了软件的结构和主要功能模块及所用的关键技术框架,并在J2EE平台上开发实现了系统。