谈谈人力资源管理信息化

文章阐述了在新经济条件下,人力资源及人力资源管理的重要性,介绍了什么是人力资源及人力资源管理,分析了我国人力资源管理的状况,以及实现人力资源管理信息化的必要性与可行性,提出了如何实现人力资源信息化的观点.     

PointSkelCNN: Deep Learning-Based 3D Human Skeleton Extraction from Point Clouds

A 3D human skeleton plays important roles in human shape reconstruction and human animation. Remarkable advances have been achieved recently in 3D human skeleton estimation from color and depth images via a powerful deep convolutional neural network. However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. In this study, we develop a deep learning-based approach for 3D human skeleton extraction from point clouds. We convert 3D human skeleton extraction into offset vector regression and human body segmentation via deep learning-based point cloud contraction. Furthermore, a disambiguation strategy is adopted to improve the robustness of joint points regression. Experiments on the public human pose dataset UBC3V and the human point cloud skeleton dataset 3DHumanSkeleton compiled by the authors show that the proposed approach outperforms the state-of-the-art methods.     

基于双目立体视觉的人体运动仿真

为辅助运动员训练,建立一个基于双目立体视觉的人体运动仿真系统。使用无标记的动作捕获算法,获取人体关节点在图像上的像素坐标。根据双目立体视觉的原理,计算出人体关节点的世界坐标,建立三维人体模型。通过求解能量函数的最优问题,优化人体模型,并仿真人体运动。实验结果表明,该系统能够较精确地完成动作捕获及运动仿真。     

人体骨架模型的建立

人体运动分析在计算机视觉研究领域中具有广泛的应用前景。人体骨架包含了很多的人体运动信息,且简单明了,所以对人体骨架提取具有很重要的意义。本文提出了一种人体骨架模型的建立方法,该方法在骨架提取方面有较强的去噪和抗干扰能力,有很好的鲁棒性,可提取更加有效的骨架图像,进一步结合人体解剖学的基础,建立了人体骨架模型,为人体特征识别(HID)和运动人体的实时追踪打下了坚实的基础。     

草绘3维人体建模的模板形变方法

目的 采用草绘交互方式直接构造3维人体模型是当前人体建模研究的重要课题之一.提出一种草绘3维人体建模的模板形变方法.方法 针对输入的草图,首先,采用关节点定位方法获取草图中的人体关节点,根据人体结构学约束识别人体骨架结构,通过解析人体轮廓草图获取人体草图特征;其次,通过骨架模板和外观轮廓模板形变,将草图特征映射到3维人体模型,实现3维人体建模.结果 草图解析方法能有效提取草图特征,通过模板形变方法生成3维人体模型,并在模型上保持草图特征;能适应不同用户的绘制习惯,且生成的3维人体模型可用于人体动画设计.结论 提出一种草绘3维人体建模的模板形变方法,支持用户采用草绘方式进行3维人体模型设计,方法具有良好的用户适应性,对3维动画创作具有重要意义.     

基于生理学的人体皮肤生成技术

随着虚拟人在科学领域的广泛应用,虚拟人体模型的仿真技术越来越精巧复杂,为能满足人体仿真效果,且技术方法快速简捷,该文借鉴人体皮肤的生理学结构,探寻出一种基于NURBS曲面的人体皮肤纹理模型的设计方法,动态模拟人体皮肤的生理变化。     

Human Motion Analysis: A Review

Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man–machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: (1) motion analysis involving human body parts, (2) tracking a moving human from a single view or multiple camera perspectives, and (3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion from a single view or multiple perspectives focuses on higher-level processing, in which moving humans are tracked without identifying their body parts. After successfully matching the moving human image from one frame to another in an image sequence, understanding the human movements or activities comes naturally, which leads to our discussion of recognizing human activities.     

着装场景下双分支网络的人体姿态估计

目的 人体姿态估计旨在识别和定位不同场景图像中的人体关节点并优化关节点定位精度。针对由于服装款式多样、背景干扰和着装姿态多变导致人体姿态估计精度较低的问题,本文以着装场景下时尚街拍图像为例,提出一种着装场景下双分支网络的人体姿态估计方法。方法 对输入图像进行人体检测,得到着装人体区域并分别输入姿态表示分支和着装部位分割分支。姿态表示分支通过在堆叠沙漏网络基础上增加多尺度损失和特征融合输出关节点得分图,解决服装款式多样以及复杂背景对关节点特征提取干扰问题,并基于姿态聚类定义姿态类别损失函数,解决着装姿态视角多变问题;着装部位分割分支通过连接残差网络的浅层特征与深层特征进行特征融合得到着装部位得分图。然后使用着装部位分割结果约束人体关节点定位,解决服装对关节点遮挡问题。最后通过姿态优化得到最终的人体姿态估计结果。结果 在构建的着装图像数据集上验证了本文方法。实验结果表明,姿态表示分支有效提高了人体关节点定位准确率,着装部位分割分支能有效避免着装场景中人体关节点误定位。在结合着装部位分割优化后,人体姿态估计精度提高至92.5%。结论 本文提出的人体姿态估计方法能够有效提高着装场景下的人体姿态...     

Human control model in teleoperation rendezvous

This paper proposes a human control model in teleoperation rendezvous on the basis of human information processing （perception, judgment, inference, decision and response）. A predictive display model is introduced to provide the human operator with predictive information of relative motion. By use of this information, the longitudinal and lateral control models for the operator are presented based on phase plane control method and fuzzy control method, and human handling qualities are analyzed. The integration of these two models represents the human control model. Such a model can be used to simulate the control process of the human operator, which teleoperates the rendezvous with the aid of predictive display. Experiments with human in the loop are carried out based on the semi-Physical simulation system to verify this human control model. The results show that this human control model can emulate human operators＇ performance effectively, and provides an excellent way for the analysis, evaluation and design of the teleoperation rendezvous system.     

基于数据决策的人力资源服务绩效模型

当前大部分电力企业人力资源服务绩效考核模式较为陈旧,绩效考核的指标较为单一.为了提高电力部门人力资源服务绩效量化管理能力,提出基于数据决策的人力资源服务绩效模型,由此加快电力部门人力资源评价机制的转变,实现电力系统人力资源管理优化配置.通过信息聚类和特征优化采样方法,构建电力系统人力资源服务绩效大数据信息分析模型;采用...     

由正交图像造型三维个性化虚拟人体模型

基于正、侧、背面4幅正交人体图像,变形模板人体模型后得到带服饰纹理的个性化虚拟人．以截面环为基本几何元素,给出了模板人体模型的生成和变形方法,建立了截面环和三维人体骨架之间的内在关联;自动提取图像上的人体特征点,生成二维图像上的人体骨架;由三维人体骨架和二维图像上人体骨架之间的对应关系,建立截面环和人体图像之间的对应关系;从人体图像上获取截面环的形状参数,通过参数化变形截面环得到个性化的虚拟人体模型．与已有方法相比,该方法建立在基于特征的参数化框架之上,变形更容易控制,消除了三维模型和二维图像之间对应的不确定性,避免了人体变形中的自交现象,效率高、真实感效果较好．     

Parts-probability-based vehicle detection

Detecting vehicles is important in aerial surveillance. Traditional methods used classifiers to detect vehicles, but a single classifier was limited to detecting vehicles of only one intensity and orientation. Therefore, the task required the use of multiple classifiers of different intensities and orientations. To solve this problem, we first used a latent Dirichlet allocation （LDA） model that improved the previous approaches to vehicle detection. Previous text modeling approaches have been generative. They could be used to build probability models of vehicles in different intensities and from various orientations simultaneously using unlabeled data. Using a probability model, we can detect vehicles in a region with high probability. Next, we used a parts-probability model that improves the LDA model. The model effectively encodes spatial structure among visual words by adding spatial relationships among vehicle parts as priors of words. A parts probability model represents a vehicle hierarchically according to parts appearances and a vehicle＇s features within the parts to enforce spatial coherency. Then, we used our model to detect vehicles from a collection of images and demonstrate its performs more effectively.     

Interactions between human–human multi-threaded dialogues and driving

In-car devices with speech user interfaces are proliferating. How can we build these interfaces such that they allow human–computer interactions with multiple devices to overlap in time, but without interfering with the driving task? We suggest that interface design can be inspired by the way people deal with this problem in human–human dialogues and propose discovering human dialogue behaviors of interest through experiments. In this paper, we discuss how to design an appropriate human–human dialogue scenario for such experiments. We also report on one human–human experiment, in terms of the dialogue behaviors found, and impact on the verbal tasks and on driving. We also offer design considerations based on the results of the study.     

An investigation of a human material handler on part flow in automated manufacturing systems

This paper presents a formal approach to resolve an important question concerning changes in the control of computerized manufacturing systems when a human operator is involved as a task-performing agent. It requires building a model of human functional specifications used in executing tasks and integrating it into a control scheme for the model. More importantly, analysis of control complexity needs to be conducted to build an effective control mechanism. In this paper, a human material handler is considered, and an assessment of part flow complexity affected by human tasks in a highly automated manufacturing system is presented. For this purpose, a formal model of human task-performing processes is proposed in terms of a part and location(s) of a task. A classification for human material handling tasks is presented based on the proposed model. Furthermore, human errors and the impact of human errors on part flow are considered. Part flow complexity of a manufacturing system from the control perspective is then investigated in terms of the human tasks and errors. A shop floor control example where a human operator performs material handling tasks is provided to illustrate the proposed model.     

Virtual human modeling from photographs for garment industry

The research presented in this paper is to develop a technique of virtual human modeling for the garment industry from two photographs of a human body in two orthogonal views. Firstly, an efficient segmentation method is applied on the two photographs to obtain the contours of the human body. After this, a template-based feature extraction algorithm is introduced to determine the feature points on the human contours by human morphology rules. Finally, a view-dependent deformation technique is described to construct the virtual human body by using human contours. Our segmentation algorithm is derived from the Mumford-Shah segmentation technology and the level set formulation, and it is accelerated by applying multi-pyramid levels. The deformation technique is related to axial deformation. With our deformation method, the reference silhouettes (the front-view and right-view silhouettes of the template human model) and the target silhouettes (the front-view and right-view silhouettes of the human body from the photographs) are used to deform the template human model, which is represented by a polygonal mesh with predefined features. The self-intersection problem in the axial deformation is solved in our deformation approach. Compared with other virtual human modeling approaches, the speed of constructing the human model is increased; and our deformation technique has better continuity and local deformation properties. At the end of the paper, some potential applications for the garment industry are given to demonstrate the functionality of virtual human models constructed by our approach.     

基于彩色聚类和人体运动知识的人体运动理解

提出了利用人体区域彩色聚类和人体运动的相关知识,理解机器视觉中人体运动的方法。根据人类视觉区分颜色的特性和实时视觉图像,自动调整区域颜色聚类的阈值;对视觉图像中的人体进行区域聚类,并分别计算出各区域聚类中心和聚类区域的质心;根据视觉图像中前后帧相对应的聚类区域质心位置变化,利用人体运动与聚类区域质心位移关系的知识,从整体或细节上理解人体的运动。     

Recognition of Human Motion From Qualitative Normalised Templates

This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching algorithm (e.g. template matching) with the proposed QNTs. The human motion is modelled by the time-varying joint angles and link lengths of an articulated human model. The ability to manage the trade-offs between model complexity and computational cost plays a crucial role in the performance of human motion classification. The QNTs is developed to categorise complex human motion into sets of fuzzy qualitative angles and positions in quantity space. Classification of the human motion is done by comparing the QNTs to the parameters learned from numerical motion tracking. Experimental results have demonstrated the effectiveness of our proposed method when classifying simple human motions, e.g. running and walking.     

基于模型的人体运动跟踪

在计算机视觉领域,人体运动分析的的研究正因其广泛的应用前景而越来越受到研究 重视,对图像序列中的人体运动进行了跟踪是其中的关键技术。由于人体运动的特殊复杂性,已有的研究方法都对人体加上了许多限制条件。     

基于多尺度特征的双层隐马尔可夫模型及其在行为识别中的应用

借鉴人类视觉感知所具有的多尺度、多分辨性的特性,针对智能视频监控系统的人体运动行为识别,提出了一种基于多尺度特征的双层隐马尔可夫模型.根据人体行为关键姿态数确定HMM的状态数目,发掘人体运动行为隐藏的多尺度结构间的关系,将运动轨迹和人体姿态边缘小波矩2个不同尺度特征应用于2层HMM,提供更为丰富的行为尺度间的相关信息.分别用Weizmann人体行为数据库和自行拍摄的室内视频,对人体运动行为识别进行仿真实验,结果表明,五状态HMM模型更符合人体运动行为特点,基于多尺度特征的五状态双层隐马尔可夫模型具有较高的识别率.