一种基于梯度方向直方图的俯视行人的检测方法

目前已有很多关于行人检测方面的研究,这些研究基本建立在行人竖直站立或行走的平视图上,主要应用于视频监控和车载辅助驾驶等领域,但在实际应用中,有时需要从不同的视角检测行人。文中提出一种针对俯视行人检测方法,该方法将俯视行人头部的梯度方向直方图统计信息作为检测目标的特征。通过训练样本提取的特征向量在支持向量机中进行训练得到分类模型参数,然后提取检测样本的特征向量输入分类模型进行判别。与现有行人检测的梯度方向直方图算子相比,文中特征描述算子突出目标的区域与轮廓特征,在目标分块、特征计算和特征统计方法上均有变化。实验证明算法有效且处理速度明显提升。     

基于边缘梯度方向直方图的图像检索算法

在基于内容的图像检索中,不同图像对形状细节的要求不同及形状特征对旋转的敏感性,影响检索性能.对此,本文提出了一种基于边缘梯度方向直方图的图像检索算法:利用B样条小波的多尺度变换模极大值进行边缘检测,实现不同图像对形状细节不同要求;用边缘梯度方向直方图表示物体的形状,采用循环移位最小值计算直方图的相关性进行图像匹配,使其具有旋转不变性.实验证明,算法对图像的亮度变化、缩放、旋转具有不变性,有较高的检索性能.     

Fast facial expression recognition using Boosted Histogram of Oriented Gradient (BHOG) features

Pattern Analysis and Applications - Systems for automatic facial expression recognition (FER) have an enormous need in advanced human-computer interaction (HCI) and human-robot interaction (HRI)...     

一种三维模型形状检索描述符

为了解决基于关键字的搜索方式不能准确查找三维模型的问题,对三角网格形式的三维模型提出一种基于内容的模型搜索算法.首先将模型中的三角网格再次划分,增加形状函数的维度,定义了一种新的三维模型形状函数,以提高形状函数的准确性;然后确定三维模型的极轴,将三维模型绕任意轴的旋转转换为绕极轴旋转,增强了球面调和方法提取的形状描述符旋转不变性;最后根据形状描述符之间的欧氏距离来度量三维模型的相似程度.实验结果表明,该算法适用于基于内容的三维模型搜索,并且在准确性和时效性方面都令人满意.     

改进的数字图像轮廓曲线傅里叶描述算法

研究图像轮廓曲线特征问题,图像轮廓曲线的描述是图像理解的一个核心问题,傅里叶描述算法是图像轮廓曲线描述的较优算法.由于传统的轮廓曲线傅里叶描述算法中曲线的采样过于复杂和不稳定,降低了识别的性能.为提高曲线描述的性能,提出了一种改进的图像轮廓曲线傅里叶描述算法,通过运用循环移位采样算法对曲线采样得到多组采样点,并取傅里叶变换后最小的一组规范化系数作为曲线的傅里叶描述子,避免了传统算法中复杂的采样过程,使算法更简洁稳定.通过算法仿真与实验对比,算法有更高的准确度和稳定性,能有效反映轮廓曲线特征.     

基于方向梯度的相关图算法在人体动作识别中的应用

针对人体动作的特征提取法,该文提出了基于方向梯度的相关图算法Correlogram of Oriented Gradient(COG),它是通过检查视频动作中的时间空间兴趣点和以兴趣点为中心的空间立方体,计算并提取空间立方体的时间空间方向梯度所具有的空间结构分布特性和外观信息,建立不同动作的特征模型,并用支持向量机(SVM)分类器来检验特征模型的识别准确率;最后,通过对比基于方向梯度的柱状图算法Histogram of Oriented Gradient(HOG),该文提出的COG算法的识别准确率比HOG算法高15%左右。     

基于Fourier描述器的汉字字形生成及合成的改进算法

对基于Fourier描述器的汉字字形生成及合成算法进行了改进。通过增加重复点和线段细分,改善了汉字的重构效果,提供了汉字笔划顺序调整、笔划起点调整和跟踪方向的一致性处理算法,处理后的汉字可完全满足使用Fourier描述器合成汉字字形的三个必要条件。     

面向领域主题的智能搜索引擎设计

本文将概念检索扩展到面向领域主题检索的范畴,提出了面向领域主题的智能检索模型。给出了概念语义网络和面向领域主题的形式化描述,利用概念语义网络实现领域主题的同义词及其语义蕴含扩展,并实现一个基于建筑业的面向领域主题的智能搜索引擎。     

企业面向电子商务的一种安全结构

电子商务的发展对企业资源的安全性和开放性都提出了更高的要求,而在传统的企业安全结构中安全性和开放性相互矛盾的,保证企业资源的安全性,就必须要牺牲企业资源的开放性,本文基于一种“复合”概念模型构筑出企业的安全结构,可以较好的解决企业资源安全性和开放性兼顾的问题。     

Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.     

基于二维直方图的图像模糊聚类分割改进方法

基于二维直方图的模糊聚类分割方法,可以有效地抑制噪声的干扰。但是,FCM算法用于图像数据聚类的最大缺陷是运算开销太大,从而限制了该算法在图像分割中的应用。针对这个问题,本文将改进抑制式FCM算法拓展到二维空间,在保持空间信息的基础上,引入了自适应抑制因子来修正不同大小的隶属度,减少了数据的聚类次数,实验结果表明该方法提高了聚类的速度,又有很好的聚类效果。     

HEDC＋＋： An Extended Histogram Estimator for Data in the Cloud

With increasing popularity of cloud-based data management, improving the performance of queries in the cloud is an urgent issue to solve. Summary of data distribution and statistical information has been commonly used in traditional databases to support query optimization, and histograms are of particular interest. Naturally, histograms could be used to support query optimization and efficient utilization of computing resources in the cloud. Histograms could provide helpful reference information for generating optimal query plans, and generate basic statistics useful for guaranteeing the load balance of query processing in the cloud. Since it is too expensive to construct an exact histogram on massive data, building an approximate histogram is a more feasible solution. This problem, however, is challenging to solve in the cloud environment because of the special data organization and processing mode in the cloud. In this paper, we present HEDC＋＋, an extended histogram estimator for data in the cloud, which provides efficient approximation approaches for both equi-width and equi-depth histograms. We design the histogram estimate workflow based on an extended MapReduce framework, and propose novel sampling mechanisms to leverage the sampling efficiency and estimate accuracy. We experimentally validate our techniques on Hadoop and the results demonstrate that HEDC＋＋ can provide promising histogram estimate for massive data in the cloud.     

Applications of the Conjugate Gradient method in optimal surface parameterizations

This paper concerns the solutions of very large symmetric semipositive definite (singular) linear systems involved in the problem of optimal surface parameterizations using inverse curvature mapping. Two approaches are presented that transform the singular linear systems into two kinds of symmetric positive definite linear systems, so that the famous Conjugate Gradient (CG) method can be used for solving them. Numerical experiments are run on two practical large problems to illustrate that the CG algorithm works very efficiently.     

一种面向对象软件缺陷的早期预测方法

软件过程早期的缺陷预测技术可以辅助软件工程决策,从而提高软件开发与测试的质量。针对面向对象软件,提出一种以分析设计模型的度量经验数据建立缺陷回归预测模型的方法,其中模型的建立使用了一种新形式的支持向量回归算法ν-SVR。为了检验缺陷预测模型的实用价值,使用了来自真实世界的Eclipse项目三个版本的度量与缺陷数据集作为模型实验的训练集与测试集。结果表明,基于面向对象分析设计模型度量建立的缺陷回归预测模型可以在生命周期早期给出有效的缺陷数预测值,从而为软件工程实践提供支持。     

An Enhanced Spring Model for Information Visualization

In this paper we present a new technique for visualizing multidimensional information. We describe objects of a higher dimensional information space as small closed free-form-surfaces in the visualization. The location, size and shape of these surfaces describe the original objects in information space uniquely. The underlying enhanced spring model is introduced. The technique is applied to two test data sets.     

An Open Environment for Real-Time Applications

This paper describes an open system architecture that allows independently developed hard real-time applications to run together and supports their reconfiguration at run-time. In the open system, each real-time application is executed by a server. At the lower level, the OS scheduler schedules all the servers on the EDF basis. At the upper level, the server scheduler of each server schedules the ready jobs of the application executed by the server according to the algorithm chosen for the application. The paper describes the two-level CPU scheduling scheme used by the open system and the design and implementation of a uniprocessor open system within the framework of the Windows NT operating system. The implementation consists of three key components: the two-level hierarchical kernel scheduler, common system service providers, and real-time application programming interface.     

An Architecture for Interactive Context-Aware Applications

The interactive behavior of context-aware applications depends on the physical and logical context in which the interaction occurs. The main difference between traditional HCI design and context-aware design is how we deal with context. In this article, we pick up on recent ubicomp community trends, drawing from sociology and focusing on interaction's communicative aspects. So, starting from the premise that interaction is communication, we propose a new interaction model for context-aware applications. We then derive an architectural framework that developers can use to implement our interaction model. The main benefit of our architecture is that, by modeling context in the user interface, developers can represent the application's inferences visually for users     

An Edge-based Stochastic Proximal Gradient Algorithm for Decentralized Composite Optimization

This paper investigates decentralized composite optimization problems involving a common non-smooth regularization term over an undirected and connected network. In the same situation, there exist lots of gradient-based proximal distributed methods, but most of them are only sublinearly convergent. The proof of linear convergence for this series of algorithms is extremely difficult. To set up the problem, we presume all networked agents use the same non-smooth regularization term, which is the circumstance for most machine learning to implement based on centralized optimization. For this scenario, most existing proximal-gradient algorithms trend to ignore the cost of gradient evaluations, which results in degraded performance. To tackle this problem, we further set the local cost function to the average of a moderate amount of local cost subfunctions and develop an edge-based stochastic proximal gradient algorithm (SPG-Edge) by employing local unbiased stochastic averaging gradient method. When the non-smooth term does not exist, the proposed algorithm could be extended to some notable primal-dual domain algorithms, such as EXTRA and DIGing. Finally, we provide a simplified proof of linear convergence and conduct numerical experiments to illustrate the validity of theoretical results.