一个文本分类构件的设计与实现

在文本分类构件设计和实现过程中,贯彻面向对象的思想,主要使用设计模式,实现构件内部的高内聚、低耦合。在构件描述方面,使用XML这一直观的手段对构件各方面的信息进行描述,在对构件文本分类功能的测试中取得较好的效果。     

基于OPENGL的树木场景渲染分析与实现

研究了基于OPENGL的树木生成算法与树木场景渲染模式,针对自然界中的植被场景中植物的三维建模难以实现高效、快速、准确的实时渲染的难题,为改善树木形态结构建模与实时渲染的效果和速度,提出了一种面向GPU的针对叶簇的渲染架构,给出了树木建模与渲染的具体实现算法,并完成了三维替代物渲染技术,结合叶簇不同形态,采用基于几何模型的枝干渲染和基于2.5维替代物的叶簇渲染,较大地提升了视觉效果的真实性,使树木建模与实时渲染效率和准确性均得到明显提高。仿真结果表明,树木场景渲染实现模式能够较好的满足图像处理需求。     

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.     

A rough margin based support vector machine

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical ν-SVM.     

Analyzing clusters of class characteristics in OO applications

The transition from Java 1.4 to Java 1.5 has provided the programmer with more flexibility due to the inclusion of several new language constructs, such as parameterized types. This transition is expected to increase the number of class clusters exhibiting different combinations of class characteristics. In this paper we investigate how the number and distribution of clusters are expected to change during this transition. We present the results of an empirical study were we analyzed applications written in both Java 1.4 and 1.5. In addition, we show how the variability of the combinations of class characteristics may affect the testing of class members.     

Augmented Panoramic Video

Many video sequences consist of a locally dynamic background containing moving foreground subjects. In this paper we propose a novel way of re‐displaying these sequences, by giving the user control over a virtual camera frame. Based on video mosaicing, we first compute a static high quality background panorama. After segmenting and removing the foreground subjects from the original video, the remaining elements are merged into a dynamic background panorama, which seamlessly extends the original video footage. We then re‐display this augmented video by warping and cropping the panorama. The virtual camera can have an enlarged field‐of‐view and a controlled camera motion. Our technique is able to process videos with complex camera motions, reconstructing high quality panoramas without parallax artefacts, visible seams or blurring, while retaining repetitive dynamic elements.     

The Visual Computing of Projector‐Camera Systems

This article focuses on real‐time image correction techniques that enable projector‐camera systems to display images onto screens that are not optimized for projections, such as geometrically complex, coloured and textured surfaces. It reviews hardware‐accelerated methods like pixel‐precise geometric warping, radiometric compensation, multi‐focal projection and the correction of general light modulation effects. Online and offline calibration as well as invisible coding methods are explained. Novel attempts in super‐resolution, high‐dynamic range and high‐speed projection are discussed. These techniques open a variety of new applications for projection displays. Some of them will also be presented in this report.     

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.     

Instance weighting versus threshold adjusting for cost-sensitive classification

In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees.     

Boosting text segmentation via progressive classification

A novel approach for reconciling tuples stored as free text into an existing attribute schema is proposed. The basic idea is to subject the available text to progressive classification, i.e., a multi-stage classification scheme where, at each intermediate stage, a classifier is learnt that analyzes the textual fragments not reconciled at the end of the previous steps. Classification is accomplished by an ad hoc exploitation of traditional association mining algorithms, and is supported by a data transformation scheme which takes advantage of domain-specific dictionaries/ontologies. A key feature is the capability of progressively enriching the available ontology with the results of the previous stages of classification, thus significantly improving the overall classification accuracy. An extensive experimental evaluation shows the effectiveness of our approach.