Tracking a Screen and Detecting Its Rate of Change in 3-D Video Scenes of Multipurpose Halls

An automatic approach is presented to track a wide screen in a multipurpose hall video scene. Once the screen is located, this system also generates the temporal rate of change by using the edge detection based method. Our approach adopts a scene segmentation algorithm that explores visual features(texture) and depth information to perform efficient screen localization. The cropped region which refers to the wide screen undergoes salient visual cues extraction to retrieve the emphasized changes required in rate-ofchange computation. In addition to video document indexing and retrieval, this work can improve the machine vision capability in the behavior analysis and pattern recognition.     

基于写相关支持向量描述的入侵防护审计模型研究

设计了基于写相关支持向量描述的安全审计模型来实现一个新的单类分类器,对系统调用中“写性质”子集进行监视和分析,并以此训练单类分类器,使偏离正常模式的活动都被认为是潜在的入侵。该模型仅利用正常样本建立了单分类器,因此系统还具有对新的异常行为进行检测的能力。通过对主机系统执行迹国际标准数据集的优化处理,只利用少量的训练样本,实验获得了对异常样本100%的检测率,而平均虚警率接近为0。     

基于迭代顺序滤波子空间约束的可拒识-支持向量机微钙化点检测

为提高钙化点检测速度,克服微钙化点检测中假阳性高的缺点,本文构造了一种迭代顺序滤波子空间约束的可拒识-支持向量机分类器用于钙化点检测.训练时利用迭代顺序滤波检测作为钙化点的粗检测算子,然后在其约束的子空间内收集非钙化点训练样本.对于输入模式,首先利用基于最大软间隔超平面的支持向量分类器(SVC)进行分类判决;然后对真实的钙化点样本特征空间求取最小的包含球形边界,得到钙化点样本的球形支持向量域表示(SVDD).对于输入模式即可利用钙化点的支持向量域表示进行拒识或接受处理.仿真实验结果表明,本文提出的算法在不影响微钙化点的检出率的情况下,大大提高了检测速度,部分解决了假阳性高的问题.     

分类器的分类性能评价指标

通过具体应用实例,指出目前普遍使用的正确率和错误率评价指标在不平衡数据集、语义相关多分、不同错分代价等分类问题中评价分类器性能时存在的缺陷。为了解决这一问题,根据具体问题的不同,提出了综合使用查准率、查全率、漏检率、误检率、F-measure和分类代价矩阵、损失函数等新的分类器性能评价指标。通过实验证明,新的分类评价指标确实能很好的适应不平衡数据集、语义相关多分、不同错分代价等分类问题的分类器性能评价。     

COVID-19 discrimination framework for X-ray images by considering radiomics,selective information,feature ranking,and a novel hybrid classifier

In medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches (Bhattacharyya, entropy, Roc, t-test, and Wilcoxon) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs. others.     

一种改进的支持向量机分类器

提出了一种将支持向量机分类和最近邻分类相结合的方法,形成了一种新的分类器.实验结果表明使用支持向量机结合最近邻分类的分类器分类比单独使用支持向量机分类具有更高的分类准确率,同时可以较好地解决应用支持向量机分类时核函数参数的选择问题.     

一种基于HOG的快速人体检测方法

Dalal等人提出的基于梯度方向直方图(HOG)的人体识别算法具有较高精度,因此成为了人体识别领域热门研究的对象,文中提出了一种快速HOG特征提取算法,并用支持向量机(SVM)训练得到人体分类器。实验结果表明,该算法在不降低检测效果的情况下,明显提高了人体检测的速度,且具有较强的鲁棒性。     

TBM掘进机配套带式输送机部件设计分析

简述了TBM掘进机机机内、机外使用短、长距离带式输送机的原因,并对两类带式输送机部件的设计进行分析,说明了带式输送机输送碴土方案适合隧道工程要求的连续、高效、经济性,及带式输送机在隧道工程的使用拓展了带式送机运用的用途.     

基于Winnow的中文邮件分类器的设计

本文首先通过建立实验用中文邮件语料库，提出了用Winnow算法实现邮件分类器，并讨论了其特点,然后，文章提出了通过建立分类规则库改善邮件分类器的分类效果．实验证明，该方法能有效提高邮件分类的可信度．     

一种基于信息复用的Boosting瀑布型分类器高效训练方法

构建一个基于Boosting的瀑布型分类器往往要花费巨大的计算代价,该文提出一种改进的Boosting瀑布型分类器训练算法,利用不同层级分类器之间的共享信息复用来提高训练速度。该文提出的瀑布型分类器训练方法在两个层次上复用已获得的层级分类器中的信息。在分类器层次上,前一个层级分类器在新的层级分类器训练过程中成为第1个特征,并在当前层级分类器的训练样本集上进行适应性学习;在特征层次上,每一级分类器都直接保留所有前面层级分类器已经学到的特征,并重新在当前层级分类器的训练样本集上进行适应性学习。最后,再通过学习新的弱分类器,加入新特征。在正面人脸检测问题上的实验表明,提出的新算法相比传统方法能够大幅提高Boosting瀑布型分类器的训练速度,提高幅度近10倍,瀑布型分类器的训练时间从将近3天降到了8个小时。