Automatic localization of craniofacial landmarks for assisted cephalometry

In this paper we propose a system for localization of cephalometric landmarks. The process of localization is carried out in two steps: deriving a smaller expectation window for each landmark using a trained neuro-fuzzy system (NFS) then applying a template-matching algorithm to pin point the exact location of the landmark. Four points are located on each image using edge detection. The four points are used to extract more features such as distances, shifts and rotation angles of the skull. Limited numbers of representative groups that will be used for training are selected based on k-means clustering. The most effective features are selected based on a Fisher discriminant for each feature set. Using fuzzy linguistics if-then rules, membership degree is assigned to each of the selected features and fed to the FNS. The FNS is trained, utilizing gradient descent, to learn the relation between the sizes, rotations and translations of landmarks and their locations. The data for training is obtained manually from one image from each cluster. Images whose features are located closer to the center of their cluster are used for extracting data for the training set. The expected locations on target images can then be predicted using the trained FNS. For each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image. The template is matched to the search windows to find the exact location of the landmark. Decomposition of landmark shapes is used to desensitize the algorithm to size differences. The system is trained to locate 20 landmarks on a database of 565 images. Preliminary results show a recognition rate of more than 90%.     

COS:度量分布式大数据处理系统的效率

[目的]在大数据处理领域,分布式计算系统得到广泛应用,它们的可扩展性得到重点关注,但其绝对性能往往没有得到重视.我们希望提出科学合理、与时俱进的度量标准,对分布式系统的性能进行评估.[方法]本文通过对比特定任务的单机实现和分布式实现来讨论分布式系统的性能,提出COS(Configuration that Outperf...     

基于视觉注意机制的感兴趣区提取方法

以精确获取图像中对象感兴趣区域为目标,提出一种基于视觉注意机制和K均值聚类相结合的感兴趣区提取方法。图像经过视觉特征提取、高斯金字塔多尺度变换后,依据多特征图合并策略生成显著图。采用K均值聚类方法分割图像的候选区域,并结合显著图提取图像感兴趣区。实验结果表明,运用该方法提取的感兴趣区更接近人类的视觉注意过程,并具有一定的抗噪能力。     

Big label: categorizing the Web efficiently and accurately

Searching on the Web has never been an easy task. Even if semantic information is successfully inferred from a user query, how can we benefit from it? The most popular remedy today is to categorize the Web in advance. By gathering similar Web resources into a group, the search performance should increase even though search engines still have little idea about the semantics part. To categorize a set of Web resources according to meta-information associated with them, at first, one has to analyze the relationships between meta-information and Web resources. However, the result will be severely affected by the ambiguous nature of the Web. As a result, the goal of this research is to propose a new labeling method to enhance both the efficiency and accuracy of Web resources categorization.     

面向云制造的资源集群聚类方法研究

针对云制造平台海量多样的服务资源分类界限模糊的问题,分析了云服务和制造资源之间的关系,提出云制造下混合式资源服务聚集模型。此外,文章基于k-means聚类算法建立了聚类有效性评估函数;针对k-means聚类算法对初始聚簇中心敏感易陷入局部最优的缺点,引入蛙跳算法确定初始聚簇中心,利用反向解扩大初始蛙群的搜索范围,结合最优解均值改进族群最差蛙的优化,提高族群的信息共享能力,结合改进后的蛙跳算法和k-means迭代,提出一种基于蛙跳算法改进的k-means聚类算法。最后,以两种数据集和云平台上同类机床资源为例,验证了所提聚类算法的有效性和可行性。     

基于密度加权的K均值聚类密钥量化方案

在物理层密钥生成过程中,现有传统的量化方案基于规则量化边界设计,无法根据实际采样测量值自适应动态调整量化边界,当量化边界不规则时边界附近量化产生的密钥不一致率较高.针对上述问题,提出一种基于密度加权的K均值聚类密钥量化方案,通过统计测量值在复平面的分布疏密情况,使量化分界远离测量值密集区域,降低噪声对量化判决的影响.仿...     

基于yolov4的垃圾检测系统

为解决垃圾分类困难问题,根据大量垃圾图片的特点,提出了基于yolov4的垃圾检测的方法,实现垃圾的自动识别分类和定位。该方法通过手机拍摄以及百度等收集不同种类的垃圾图像集,运用图像增强的方式进行图像预处理,再利用迁移学习模型和k-mean++聚类算法进行垃圾网络分类模型的训练测试,最后将训练好的模型通过摄像头对采集的垃圾图片实时识别分类和定位。实验表明:该方法的mAP达到95.15%,能够快速有效地对垃圾分类和定位。     

基于最小聚类单元的聚类算法研究及其在CRM中的应用

将聚类分析技术应用于客户关系管理可以改善客户关系,对将来的趋势和行为进行预测,优化营销策略。在综合分析网格聚类算法和K-均值聚类算法的基础上,提出了基于最小聚类单元（Minimum Clustering Cell,简称MCC）的聚类算法,介绍了该算法在CRM中的应用。经证明该算法是一种实用的、速度更快、效率更高的改进聚类算法,它克服了K-均值聚类需要事先给定K值、网格聚类要求数据密集的缺点。     

面向车辆边缘计算的任务合作卸载

随着物联网技术和人工智能技术的飞速发展,车辆边缘计算越来越引起人们的注意。车辆如何有效地利用车辆周边的各种通信、计算和缓存资源,结合边缘计算系统模型将计算任务迁移到离车辆更近的路边单元,已经成为目前车联网研究的热点。由于车辆应用设备有限的计算资源,车辆用户的任务计算需求无法满足,需要充分利用车辆周边的计算资源来计算任务。本文研究了车辆边缘计算中任务的合作卸载机制,以最小化车辆任务的计算时延。首先,设计了任务合作卸载的三层系统架构,考虑了车辆周边停泊车辆的计算资源以及路边单元的计算资源,组成云服务器层、停泊车辆合作集群层和路边单元合作集群层的三层架构。通过路边单元合作集群和停泊车辆合作集群的合作卸载,充分利用系统的空闲计算资源,进一步提高了系统的资源利用率。然后,基于k-聚类算法的思想提出了路边单元合作集群划分算法对路边单元进行合作集群的划分,并采用块连续上界最小化的分布式迭代优化方法设计了任务合作卸载算法,对终端车辆用户的任务进行卸载计算。最后,通过将本文算法和其他算法方案的进行实验对比,仿真结果表明,本文算法在系统时延和系统吞吐量方面具有更好的性能表现,可以降低23%的系统时延,并且能提升28%的系统吞吐量。