基于形状匹配的卡通运动捕捉

提出一种对传统的卡通动画进行运动捕捉的方法．不同于以前基于骨架模型或者关键形状表示的方法,用整体仿射运动和局部非仿射变形的组合来表示卡通人物的运动,用卡通人物的轮廓表示它的形状;然后直接对相邻的关键帧进行形状匹配,将恢复的运动参数映射到一个目标人物上,使其具有与原来人物相同的运动方式．文中不使用先验模型,通过形状匹配来获取和映射卡通人物的运动,并通过实验验证了该方法的可行性．     

基于三维人体语义骨架点的姿态匹配

针对三维人体模型结构复杂,处理数量大且不易提取控制点等问题,提出通过对人体形状进行特征分析描述人体结构并进行姿态识别的算法。融合测地线与空间结构等特征提取骨架点有效减少数据的计算量,并通过ICP算法进行姿态的行为识别。实验证明,该算法有效地提升了三维姿态的识别效率,并有很好的鲁棒性。     

基于特征匹配的单目相机姿态解算

本文采用了一种基于AKAZE特征检测和PnP算法的单目视觉测量方法对相机的相对姿态进行解算,用于快速准确地确定空间中两个目标间的位姿关系.采集合作目标的模板图像,提取附加到合作目标上的4个特征点的像素坐标,利用AKAZE关键点对模板图像和待测图像进行匹配并计算映射矩阵,通过映射矩阵得到4个特征点在待测图像中的像素坐标,然后结合合作目标的尺寸信息求解基于4个共面特征点的PnP问题,解算相机与合作目标的相对位置.实验分析表明该方法计算的实时图像相机位姿与真实结果接近,验证了本文方法的有效性.     

基于投影匹配的目标姿态测量方法研究

提出一种在图像投影匹配基础上进行的目标姿态测量新方法,避免了传统姿态测量中左右像面目标的特征匹配或灰度匹配.二维投影相关法是基于二维投影的灰度相关匹配算法,主要利用匹配图像相邻像素的灰度值的大小关系应该相同的原理进行图像匹配.在此基础上采用双目视觉测量空间轴对称目标姿态,应用面面交会法获取轴对称目标在像面的轴线,进行三维姿态测量.模拟实验结果表明:该方法姿态角测量误差小于0.2°;且计算速度快,结果稳定,能够满足处理的要求.     

基于惯性动作捕捉的人体运动姿态模拟

针对人体运动姿态编辑的自由性,提出一种人体运动姿态模拟方法。该方法采用贝塞尔曲线和数值数据编辑人体运动姿态。根据人体运动的特点,在VC++中运用OpenGL构建虚拟人体模型,利用动作捕捉技术设计人体运动姿态的模拟程序。结合人体关节正常活动范围,对主要关节点的运动姿态进行分析,结果表明,该方法能有效利用人体运动数据,驱动虚拟人体模型。     

基于动作捕捉传感器的民族舞蹈动作自动识别系统

针对传统舞蹈动作捕捉和自动识别准确率低的问题,设计一个基于动作捕捉传感器的民族舞蹈动作自动识别系统。系统通过构建人体动作数据库,为人体关节动作模型提供数据参考,利用传感器读取数据后,将读取数据置入三维人体动作模型中,将其与数据库中的标准动作进行匹配,找出舞蹈训练者的错误动作并进行纠正,以此实现舞蹈动作自动识别。测试结果表明,对比于其他动作识别系统,本系统在动作识别角度和关节点定位方面与Kinect标准值间的误差最小,识别准确率高达97.6%,综合分析可知,本系统可实现民族舞蹈动作的精准捕捉和自动识别,具有一定的有效性。     

基于MobileNetV3与ST-SRU的危险驾驶姿态识别

针对危险驾驶行为引起的交通安全事故频发的现状,提出一种基于MobileNetV3和ST-SRU的危险驾驶姿态识别系统.首先,修改MobileNetV3的网络结构使其适用于人体姿态估计任务,输出关节点的热力图和偏移量图,用来估计J个关节点的二维坐标位置;其次,定义ST-SRU骨架动作识别算法,利用动作的骨架序列数据对动作进行分类.实验结果表明:MobileNetV3姿态估计算法在自建的AI Challenger上肢姿态数据集上测得PCP值(percentage correct parts)达到95.6%,测试1 000次用时仅为5.03 s;利用自建的危险驾驶行为数据集将训练好的姿态估计和动作识别模型移植到嵌入式平台,实现了实时的危险驾驶姿态识别系统.     

基于SMPL模型的人体姿态估计

针对目前人体形变模型中姿态估计算法容易出现误差、信息缺失等问题,提出一种利用深度相机获取的人体三维信息来优化模型的方法.通过深度相机Kinect获取的三维骨架信息,与SMPL模型进行配准,修正原始的模型姿态,得到一个接近人体真实姿态的模型.实验结果表明,融合人体三维信息后,模型的准确性得到一定程度上的提高.     

基于RGBD的实时头部姿态估计

摘 要：实时的头部姿态估计在人机交互和人脸分析应用中起着至关重要的作用,但准确 的头部姿态估计方法依然具有一定的挑战性。为了提高头部姿态估计的准确性和鲁棒性,将基 于几何的方法与基于学习的方法相结合进行头部姿态估计。在人脸检测和人脸对齐的基础上, 提取彩色图像几何特征和深度图像的局部区域深度特征,再结合深度块的法线和曲率特征,构 成特征向量组;然后使用随机森林的方法进行训练;最后,所有决策树进行投票,对得到的头 部姿态高斯分布估计进行阈值过滤,进一步提高模型预测的准确度。实验结果表明,该方法与 现有的头部姿态估计方法相比,具有更高的准确度及鲁棒性。     

基于姿态转换网络的行为识别

为了有效的表征行为,提出了一种基于姿态转换网络的行为识别算法。首先对人体进行自动定位,并对人体区域进行形状与运动特征提取;然后对特征进行层次聚类,构建姿态二叉树,并将运动序列表示为姿态序列后,将其表征为姿态转换网络的权重;最后利用k-近邻的方法对行为进行分类识别。实验结果表明,该算法对动态嘈杂背景,人体执行行为速度的快慢具有一定程度的鲁棒性。该算法在两个公用数据库上获得了较好的结果验证了其有效性。     

A marker-less assembly stage recognition method based on corner feature

Fast and robust product assembly stage recognition is a key step in human–machine cooperative assembly. To solve the recognition problem of similar adjacent assembly stages, a marker-less assembly stage recognition method is proposed based on corner feature between an assembling product and a digital model. Considering the geometric features of mechanical products, a corner identification method is proposed based on the circumferential angle difference (CADF). Then a corner matching method based on distance constraint is studied for ICP registration to realize the point cloud registration between the product and digital model. Based on the registration relationship, a similarity algorithm based on proximity point proportion is used to calculate the similarity between models and the input assembling product. The model with the greatest similarity is taken as the stage recognition result. In experiments on four group assembling products, the average stage recognition accuracy is 96.15%, which indicates that the proposed method can solve the stage recognition problem. The corner identification method based on the CADF outperforms the Harris-3D corner detection method in the efficiency of assembly stage recognition.     

An efficient intrusion detection system based on support vector machines and gradually feature removal method

The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161.     

Combined VMD-SVM based feature selection method for classification of power quality events

Power quality (PQ) issues have become more important than before due to increased use of sensitive electrical loads. In this paper, a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems. The proposed method is constructed based on four main steps: simulation of PQ events, extraction of features, selection of dominant features, and classification of selected features. By using two powerful signal processing tools, i.e. variational mode decomposition (VMD) and S-transform (ST), some potential features are extracted from different PQ events. VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains. In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure, sequential forward selection (SFS) and sequential backward selection (SBS) as wrapper based methods and Gram–Schmidt orthogonalization (GSO) based feature selection method as filter based method are used for elimination of redundant features. In the next step, PQ events are discriminated by support vector machines (SVMs) as classifier core. Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions. Moreover, the start and end points of PQ events can be detected with high precision.     

以脑电图子序列特征向量为节点的脑网络分析方法

工作记忆复杂网络分析方法大多数是以通道作为节点从空间的角度进行分析，很少有从时间角度对通道网络进行分析。针对脑电图（EEG）的高时间分辨率特性及时间序列分段较难的缺陷，提出一种从时间角度构建网络并对网络进行分析的方法。首先，利用微状态将每个通道的EEG信号划分成不同的子段作为网络的节点；其次，在子段中提取并选择有效特征作为子段的特征，计算子段特征向量之间的相关性构建通道时间序列复杂网络；最后，对所构建网络的属性及相似性进行分析，并在精神分裂症患者EEG数据上进行验证。实验结果表明，通过所提方法对精神分裂症数据进行分析，能够充分利用EEG信号的时间特性从时间角度深入了解精神分裂症病人工作记忆中构建的时间序列通道网络的特点，解释了精神分裂症患者与正常人的显著性差异。     

A clustering method based on multidimensional texture analysis

Considering the analogy between image segmentation and cluster analysis, the aim of this paper is to adapt statistical texture measures to describe the spatial distribution of multidimensional observations. The main idea is to consider the cluster cores as domains characterized by their specific textures in the data space. The distribution of the data points is first described as a multidimensional histogram defined on a multidimensional regular array of sampling points. In order to evaluate locally a multidimensional texture, a co-occurrence matrix is introduced, which characterizes the local distribution of the data points in the multidimensional data space. Several local texture features can be computed from this co-occurrence matrix, which accumulates spatial and statistical information on the data distribution in the neighborhoods of the sampling points. Texture features are selected according to their ability to discriminate different distributions of data points. The sampling points where the local underlying texture is evaluated are categorized into different texture classes. The points assigned to these classes tend to form connected components in the data space, which are considered as the cores of the clusters.     

A linear discriminant analysis method based on mutual information maximization

We present a new linear discriminant analysis method based on information theory, where the mutual information between linearly transformed input data and the class labels is maximized. First, we introduce a kernel-based estimate of mutual information with a variable kernel size. Furthermore, we devise a learning algorithm that maximizes the mutual information w.r.t. the linear transformation. Two experiments are conducted: the first one uses a toy problem to visualize and compare the transformation vectors in the original input space; the second one evaluates the performance of the method for classification by employing cross-validation tests on four datasets from the UCI repository. Various classifiers are investigated. Our results show that this method can significantly boost class separability over conventional methods, especially for nonlinear classification.     

基于对比度分析及矢量匹配的混合高斯模型

针对经典的混合高斯背景建模算法鲁棒性不强且背景建模实时性不足的特点,提出了一种改进方法。首先将图像矢量化,即将图像分成若干块,每一块图像作为一个矢量进行整体建模;然后对于每一个图像块基于其反差描述元与K个高斯模型进行匹配。实验结果表明,改进的算法降低了环境光变化和背景波动等因素的干扰且建模速度较快。     

基于可伸缩矢量图形的手绘几何图形结构分析方法

复杂图形通常是由多个图元按一定几何关系构成,以基本图形的识别为基础,复杂图形识别重点在于图形元素之间的空间关系模式的判定。几何图形的图元构成复杂,难以直接利用启发式规则进行识别;而现有的结构分析方法太复杂,采用传统方法难以进行有效识别。针对手绘几何图形识别中结构分析这一核心技术问题,设计了一种几何图形结构描述模型,该模型通过对图元及其约束关系的形式化描述来表示图形,使用可伸缩矢量图形(SVG)标签存储图元及其约束,通过解析SVG标签来识别几何图形的形状及其内部关系,为图形结构分析提供了统一格式的表示方法。所提方法已经过自主开发的GeoSketch系统的验证,并取得良好效果。实验结果表明:该方法简洁、低维,方便进行图形形状及内部关系的判定。     

基于典型相关分析方法的尺度不变特征变换误匹配剔除

针对尺度不变特征变换(SIFT)描述子仅利用特征点的局部邻域灰度信息而对图像内具有相似灰度分布的特征点易产生误匹配的问题,提出一种基于典型相关分析(CCA)的SIFT误匹配剔除方法.该方法首先利用SIFT算法进行匹配,得到初始匹配对; 然后根据典型相关成分的线性关系拟合直线,利用点到直线的距离剔除大部分误匹配点对; 对剩余的匹配点对,逐一分析其对典型相关成分的共线性的影响,剔除影响程度大的特征点对.实验结果表明,该方法能够在剔除误匹配的同时保留更多的正确匹配,提高了图像配准的精度.