M2+-树:一种支持医学病例多度量空间检索的高效索引

由于从病例库中进行病例的相似性检索关系到能否提供给医生充分且正确的候选病例,因此如何高效、准确地实现影像病例的相似性检索是学术界和医学界的研究热点之一.迄今为止,很多文献提出了用于提高查询精度的检索策略,但涉及检索效率的文章还为之甚少.基于此,提出了一种融多种度量空间相似性计算于一体的M2+-树高维索引技术.该索引将病例中的文本和影像合成一个高维多特征向量,该向量在度量空间上将数据空间划分成若干子空间,并借助关键向量对划分后的数据子空间再进行向量空间上的二次划分.关键向量的无重叠划分和三角不等式过滤原理可以加快病例的检索速度.总之,在度量和向量空间上的两次数据划分使得M2+-索引树大大减少了待查询病例与数据库病例间的不必要相似性计算的次数,从而加快了相似性病例的检索速度.实验结果表明,M2+-树的性能优于典型的度量空间多特征索引代表M2-树的性能.     

一种新的运动目标快速检测分割方法的研究

运动目标的检测与分割是视频分析的重要内容.对静态背景中的运动对象的检测方法进行了研究,针对基于混合高斯模型的背景减除法无法解决的"鬼影"和算法复杂耗时的问题,提出了一种新的基于帧差运动边缘检测的方法.实验证明,该方法可以在复杂背景下准确地获得运动对象边界,大大提高检测速度,同时有效消除背景光照变化及个别景物扰动带来的干扰.采用模板填充算法分割运动目标,并通过数学形态学滤波去除运动区域内的噪声点和填补空洞,获得完整理想的运动对象区域.     

基于形式概念分析与分类树法的Web应用测试方法

Web应用软件开发周期短,时间和资源条件有限,要求有高效的测试。应用分类树法从Web应用导航模型自动生成测试用例,并应用形式概念分析对生成的测试用例集进行精简,在满足给定覆盖标准的前提下,得到较小的测试用例集,提高测试效率。     

Perceptual-saliency extremum lines for 3D shape illustration

Owing to their efficiency for conveying perceptual information of the underlying shape and their pleasing perceiving in visual aesthetics experience, line drawings are now becoming a widely used technique for illustrating 3D shapes. Using a center-surrounding bilateral filter operator on Gaussian-weighted average of local projection height between mesh vertices and their neighbors, a new perceptual-saliency measure which can depict surface salient features, is proposed in this paper. Due to the definition of perceptual-saliency measure, our perceptual-saliency extremum lines can be considered as the ridge-valley lines of perceptual-saliency measure along the principal curvature directions on triangular meshes. The experimental results demonstrate that these extremum lines effectively capture and depict 3D shape information visually, especially for archaeological artifacts.     

Linear discriminant projection embedding based on patches alignment

Dimensionality reduction is often required as a preliminary stage in many data analysis applications. In this paper, we propose a novel supervised dimensionality reduction method, called linear discriminant projection embedding (LDPE), for pattern recognition. LDPE first chooses a set of overlapping patches which cover all data points using a minimum set cover algorithm with geodesic distance constraint. Then, principal component analysis (PCA) is applied on each patch to obtain the data's local representations. Finally, patches alignment technique combined with modified maximum margin criterion (MMC) is used to yield the discriminant global embedding. LDPE takes both label information and structure of manifold into account, thus it can maximize the dissimilarities between different classes and preserve data's intrinsic structures simultaneously. The efficiency of the proposed algorithm is demonstrated by extensive experiments using three standard face databases (ORL, YALE and CMU PIE). Experimental results show that LDPE outperforms other classical and state of art algorithms.     

现代空间环境中屏风设计理念的拓展和延伸

本文着重论述了在现代空间环境中如何拓展和延伸屏风的设计理念,使其在丰富多元的现代社会中具有更广阔的发展空间,并符合现代人的需求。     

通过实践教学培养学生综合能力

从人才培养需求出发,通过实际的教学实例,探索在网络程序设计课程实践教学过程中培养学生综合能力的方式方法。提出具体的可操作的实践课教学方法,希望对该课程实践课教学有一定的借鉴作用。     

Two-level hierarchical combination method for text classification

Text classification has been recognized as one of the key techniques in organizing digital data. The intuition that each algorithm has its bias data and build a high performance classifier via some combination of different algorithm is a long motivation. In this paper, we proposed a two-level hierarchical algorithm that systematically combines the strength of support vector machine (SVM) and k nearest neighbor (KNN) techniques based on variable precision rough sets (VPRS) to improve the precision of text classification. First, an extension of regular SVM named variable precision rough SVM (VPRSVM), which partitions the feature space into three kinds of approximation regions, is presented. Second, a modified KNN algorithm named restrictive k nearest neighbor (RKNN) is put forward to reclassify texts in boundary region effectively and efficiently. The proposed algorithm overcomes the drawbacks of sensitive to noises of SVM and low efficiency of KNN. Experimental results compared with traditional algorithms indicate that the proposed method can improve the overall performance significantly.     

A conjugate bayesian approach for calculating process capability indices

Process capability indices measure the ability of a production process to produce items within specification limits. The calculation of process capability indices has been focusing on using traditional frequency approach, which requires a large sample size for an accurate estimation. In order to eliminate this defect of traditional frequency approach on multi-batch and low volume production, Bayesian approach was used. The conjugate Bayesian approach is chosen to estimate the process distribution parameters. The algorithm with these conjugate Bayes estimators is proposed for measuring the process capability for multi-batch and low volume production. A case study is presented to demonstrate how the approach can be applied to actual data collected in practice.     

A rough set approach to feature selection based on power set tree

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Traditional hill-climbing search approaches to feature selection have difficulties to find optimal reducts. And the current stochastic search strategies, such as GA, ACO and PSO, provide a more robust solution but at the expense of increased computational effort. It is necessary to investigate fast and effective search algorithms. Rough set theory provides a mathematical tool to discover data dependencies and reduce the number of features contained in a dataset by purely structural methods. In this paper, we define a structure called power set tree (PS-tree), which is an order tree representing the power set, and each possible reduct is mapped to a node of the tree. Then, we present a rough set approach to feature selection based on PS-tree. Two kinds of pruning rules for PS-tree are given. And two novel feature selection algorithms based on PS-tree are also given. Experiment results demonstrate that our algorithms are effective and efficient.