基于视觉点特征的图像检索技术研究

基于视觉点特征的图像检索是图像检索(Content Based Image Retrieval,简称CBIR)的一个子集,指利用图像中对图像的内容有显著影响的一些点特征对图像进行查询,试图在理解图像内容的基础上,检索出与示例相类似的图像。目前,CBIR技术在商标查询、罪犯比对等领域有着很重要的应用。本文主要集中在对图像的感兴趣点特征提取技术上,针对边缘方向直方图法存在的问题,提出一种基于Canny边缘提取及轮廓序列矩法,首先对图像进行预处理,然后采用Canny边缘提取方法提取图像的轮廓,最后,将该轮廓的3个矩作为最后的特征向量进行检索。实验证明,提出的算法检索效果较好。     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

一种基于混合匹配的指纹识别方法

为克服传统的基于细节点匹配的不足,对基于点模式匹配算法与改进的2DPCA匹配算法的混合识别算法进行了改进。改进后的算法在点模式匹配算法中加入改进的2DPCA算法的初匹配得分权重,提高了点模式匹配算法的准确性;并利用点模式匹配算法对2DPCA算法的匹配结果进行二次匹配,同时也提高了2DPCA算法匹配的准确率。     

维吾尔语广播新闻连续语音敏感词检索系统

首先介绍语音信号来源于新疆人民广播电台维吾尔语新闻的敏感词语音语料库的建设。然后用该语料库进行基于HMM的模型训练。模型训练中详细介绍识别基元端点检测、特征提取、矢量量化、码本构建、HMM模型训练过程和结果。最后用该语料库和HMM训练模型对维吾尔语广播新闻连续语音信号进行敏感词检索,并对检索结果进行分析。     

基于Web的虚拟旅游环境的开发及其关键技术*

首先介绍了虚拟旅游的基本概念、研究意义、开发模式和主流开发平台,进而针对虚拟旅游的关键技术作了综述性研究,最后对虚拟旅游将来的开发方向进行了展望性研究。     

一种自适应的数字图像信息隐藏算法

给出一种新的自适应数字图像信息隐藏算法.在传统混合算法的基础上,根据公开图像本身的像素分布和颜色特点,对混合系数进行改变.算法结合图像的局部特征,充分利用人类视觉的各种掩蔽特性自适应地嵌入秘密信息,对原图像所作的改变具有很好的不可见性.算法简单、信息隐藏量大,且具有一定的鲁棒性.     

基于RSA密码体制构造一类新型的CEMBS

公平性是电子商务协议的基本安全要求之一,CEMBS是一种重要的用于构造公平交换协议的密码部件,分析了现有的CEMBS构造方法中存在的问题,首次提出了一种基于RSA密码体制的简洁、高效、安全的CEMBS构造方法,在此基础上,利用这一新型的CEMBS构建了一类重要的公平交换协议。     

基于ICA与Bayes的判别分析模型

简要介绍了Bayes判别分析模型的特点及存在的问题,概括了独立成分分析(ICA)的特点及发展现状,提出了基于ICA与Bayes的判别分析模型--IBD模型.该模型首先利用ICA的方法将相关性数据指标转换为互相独立的数据指标,并通过卡尔曼滤波方式滤去高频数据,有效地去除了噪声,最后利用Bayes方法对转换的数据进行判别分析.实验结果表明,当数据之间存在相关关系时,IBD模型的判别分析效果要优于Bayes与Fisher判别分析模型.     

PMMA/ASA合金的制备及其性能研究

通过熔融共混的方法制备了聚甲基丙烯酸甲酯(PMMA)/丙烯腈-苯乙烯-丙烯酸酯三元共聚物(ASA)合金。研究了ASA的熔体质量流动速率及其与PMMA的比例对合金力学性能、加工流动性的影响。结果表明:选用高熔体质量流动速率ASA的合金其拉伸强度和弯曲强度与选用低熔体质量流动速率ASA的合金相差不大,但冲击强度明显高于后者。DSC分析表明,低熔体质量流动速率的ASA在48.54℃出现一个玻璃化转变(Tg)平台,其对应于接枝有SAN的丙烯酸酯橡胶颗粒与SAN基体之间形成的界面相的Tg平台。SEM进一步表明,选用低熔体质量流动速率ASA的合金其冲击断面较光滑,呈现脆性断裂的特征。纳米氧化铝的添加提高了PMMA/ASA合金的耐摩擦磨损性能及表面硬度。