Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction

We analyze a variational approach to image segmentation that is based on a strictly convex non-quadratic cost functional. The smoothness term combines a standard first-order measure for image regions with a total-variation based measure for signal transitions. Accordingly, the costs associated with discontinuities are given by the length of level lines and local image contrast. For real images, this provides a reasonable approximation of the variational model of Mumford and Shah that has been suggested as a generic approach to image segmentation.The global properties of the convex variational model are favorable to applications: Uniqueness of the solution, continuous dependence of the solution on both data and parameters, consistent and efficient numerical approximation of the solution with the FEM-method.Various global and local properties of the convex variational model are analyzed and illustrated with numerical examples. Apart from the favorable global properties, the approach is shown to provide a sound mathematical model of a useful locally adaptive smoothing process. A comparison is carried out with results of a region-growing technique related to the Mumford-Shah model.     

论原型结构设计的科学性和时代性

本文以日本文化式原型为基础,从原型的“以人为本”之设计原则,合理化简历化的修订方针,论述了原型法结构设计的科学性。同时以其“切展手法”和系列化多样化满足时装变化和服装业发展之需,论证了原型法服装结构设计的时代适应性。     

Feature sequencing in the rapid design system using a genetic algorithm

This paper addresses the feature sequencing problem in the Rapid Design System (RDS). The RDS is a feature-based design system that integrates product design and process planning. An important issue in feature-based process planning for machined parts is the order in which material is removed to form the resultant part. The order, or sequence, is partially dependent on the geometric relationships between features. The sequence affects the safety, the time it takes to machine the part, and the quality of the finished part. The sequence of material removal depends on two types of relations between features: (1) intersections and (2) interfeature associations. Both types of relations compound the search for an optimal sequence of material removal. Therefore, the research problem has been the discovery and development of a genetic algorithm (GA) that efficiently searches the solution space for all possible sequences and identifies the best sequences in terms of safety, time and quality.     

僵尸网络检测技术研究

由于全球网络都在遭受日益广泛的僵尸网络所带来的威胁,因此,必须加大对其防治.僵尸网络检测的方法分为三个部分:“僵尸网络静态特征检测技术”、“僵尸网络动态特征检测技术”、“僵尸网络混合特征检测技术”,分别指出了僵尸网络静态特征检测技术的缺陷就是误报率高、僵尸网络动态特征检测技术缺乏先验性和处理数据量大等缺点,最后指出,把...     

钓鱼网页特征向量提取方法研究

随着电子商务和在线交易的增加,网络钓鱼已经成为最严重的一种网络犯罪形式。文章从网页中包含的超链接这一角度出发,给出了网页的身份特征,并结合网页ICP号,版权所有者以及网页行为等对网页特征进行了提取,得到了钓鱼网页的特征向量,为及时准确检测钓鱼网页提供了依据。     

基于行扫描的炮弹引信铆点特征提取

在炮弹引信铆点机器视觉识别中,有效消除图像中各种干扰、正确提取铆点特征是关键,解决好铆点特征的准确提取问题,是炮弹引信安全破除铆点的前提条件;根据铆点的结构特点,分析了产生各种干扰的原因及特点,在高斯滤波,阀值分割、形态学运算等基本运算的基础上,提出了一种基于行扫描的铆点特征提取方法;测试结果表明,提出的方法可行,铆点正确识别率达到90%以上,具有高效性和很好的鲁棒性的铆点特征提取,使得炮弹铆点的准确破除成为可能.     

基于液滴指纹图的波形分析算法的改进

不同的液体在相同的夜滴分析系统中,光纤信号的强弱不同,电容信号的强弱即液滴体积不同,液滴指纹图的波峰波谷的高度、位置以及峰的形状不同,液滴指纹图的曲线长度和曲线下的面积不同;因此,对光电液滴指纹图进行波形分析,计算波形参数,可以实现特征提取并区分不同液体.针对波形分析算法在特征提取过程中存在的两个缺陷,即最值检测时可能错误识别波谷位置和邻域比较时产生大量等值数据集,提出了改进的波形分析算法;对改进前后的波形分析算法进行了分析比较,改进后的算法有效提高了波形参数的准确性.     

基于核的监督非保局投影镜头边界检测

针对如何进一步提高镜头边界检测精度问题,提出了一种基于核的监督非保局(KSNLPP)投影视频镜头检测方法.在非保局投影(NLPP)中引入样本的类别信息,并利用核方法提升NLPP解决非线生问题的能力,提出了KSNLPP算法;在此基础上,将每个镜头视为一类,利用KSNLPP算法得到视频图像的投影矩阵,利用此投影矩阵对新的视...     

几种脱机手写女书特征提取法的比较研究

目前,OCR技术已取得了多项研究成果,但是几乎没有人从事女书文字识别的研究。女书的特征提取是女书文字识别中的一个重点和难点。文章通过比较几种特征提取算法,提出一种基于G-DCD改进的特征提取算法,并用该算法正确提取手写体女书文字特征。