人体运动的数据采集系统及识别设计

人体运动的数据采集系统可实时监测人体日常活动，为人体运动健康提供了更科学的辅助。利用Shimmer无线可穿戴传感器设备采集运动数据，建立了监测人体运动的8个部位传感器节点模型，并构建了10个具有代表性的动作进行识别。用中值滤波算法实现对原始信号的过滤处理，采用固定滑动窗口分割法对数据进行分割处理，结合特征提取技术来获取包含特性的特征向量，选择主成分分析（PCA）技术对特征提取后的特征向量进行降维处理。同时，提出了多节点运动识别模型，利用支持向量机（SVM）、k近邻（kNN）以及最近距离中心算法（NCC）三种不同的算法对单独节点和组合节点的情况下的传感器数据进行分类识别。实验结果表明，所提出的人体运动数据采集系统具有良好的效果，在使用相同识别算法的前提下，多节点组合识别能获得比单节点识别更好的效果。     

基于虚拟网格的无线传感器网络数据融合算法

无线传感器网络的能量供应和通信带宽等资源相对有限,难以适应大量数据的传输,需要在网络内部对原始监测数据进行压缩或聚合处理.为了充分利用其空间和时间相关性来进行数据压缩,提出了一种基于虚拟网格的数据融合算法.该算法基于虚拟网格来构建采集数据矩阵,并分别利用时域差分和二维离散余弦变换来去除时间和空间冗余.仿真实验和理论分析表明:该算法具有良好的压缩性能,有效地降低了节点能耗和提高了网络生命周期.     

一种基于误差控制的网格多分辨模型生成算法

提出一种网格多分辨模型生成算法．该算法以最大误差L∞控制的网格简化算法为基础,通过删除边和拆分点操作进行向下和向上采样,将网格模型表示为由一个低分辨率的网格和一系列修改操作组成的多分辨模型．同其它算法相比,该算法在初始向下采样时,重点考虑了简化误差对模型精度的影响．在生成网格多分辨模型时,该算法将细化操作分解为对网格模型的几何修改信息和各细化操作之间的关系信息,确保了多分辨模型的健壮性．该算法可通过三角片数和简化误差两种方法来调整网格模型分辨率,实验结果证明了本算法的有效性．     

一种改进的GP-CLIQUE自适应高维子空间聚类算法

GP-CLIQUE算法是基于高斯过程的CLIQUE改进算法,但是此算法中存在固定划分网格和人为输入密度阈值这两个不足.提出了一种改进GP-CLIQUE的算法——REG-CLIQUE算法.该算法利用相对熵对每一维数据进行自适应网格划分,引入二叉树存储信息,删除冗余维,解决了固定划分网格的缺陷,提高了聚类结果的精度;同时,提出密度阈值的计算公式,根据数据集本身用递归算法计算密度阈值,解决了人为输入的不足,大大降低算法对先验知识的依赖性.实验结果表明,该算法在时间、聚类准确度等方面都优于GP-CLIQUE算法和CLIQUE算法.     

渐进网格及其在移动计算中的应用

在移动计算中,3维图形通常是由几何造型的网格来表示。为了解决移动图形的存储、传输和显示问题,提出了一种基于逆细分的构建渐进网格的算法,给出了渐进网格通过网格传输和在移动终端上渲染3维图形的方法。细密的网格通过逐层地、分批地删除其冗余信息,最后生成由基网格和一系列误差值组成的渐进网格。在算法实施时,将Loop逼近型细分模式作为插值型细分模式进行操作。该算法共分3个关键步骤:网格分裂、奇点预测、网格更新。简化后的渐进网格可以无损还原。实验结果表明,该算法效率高,比以往的方法速度快。     

散乱数据点集的三角划分算法研究

在对当前的三角网格划分方法进行比较分析后,提出了一种散乱数据点集的3D三角网格划分算法,该算法不需如同二维划分方法一样要对散乱点集对应的自由曲面分片投影,并可自然处理含有凹边界及孔域的曲面数据点集,利用网格扩展、边界环分裂和边界环封闭,根据曲面的变化逐层推进生成三角网格,使算法能方便地处理非封闭曲面、空间剪裁曲面、封闭曲面、空间多连通曲面等各种曲面的散乱数据。     

可视化虚拟人体图像规整化处理

可视化虚拟人体数据集规整化处理有着重要意义。本文首次对其断层图像的规整化进行了尝试：建立其三角形控制网格,采用图形领域的重心坐标变换方法,对其进行图像位置变换。该图形图像混合变换算法,算法稳定,操作简单,规整化处理效果明显。     

激光三维扫描数据的表面重建

对激光三维扫描系统获得的没有任何附加信息的轮廓线点云数据进行处理，首先采用求最大连通域的方法删除噪声点，利用设定相邻点连线夹角正切阈值的方法精简数据，然后采用基于局部切平面簇的方法对数据点云进行切平面的估算、法向量的调整和计算距离函数，用改进的MC方法输出三维网格，并且应用基于顶点的网格删除算法对三维网格进行简化，在估算切平面的时候采用新的估算原则，提高了重建速度，改善了重建效果，所表述的重建流程，成功地解决了激光扫描系统所得轮廓数据点的表面重建问题。     

基于n边形折叠的网格简化算法

提出在三角网格中利用多个三角形组合及检索n边形(n为正整数)的规则,并提出一种具有相似折叠规律的n边形折叠的网格简化算法,该算法以n边形折叠为基本简化操作,并以二次误差作为误差度量,每次n边形折叠操作可以减少,n-1个顶点以及2(n-1)个三角形,n越大达到某一简化目标所需的折叠次数越少,因此简化速度也可能越快.通过选取适当的n值及新顶点位置,新算法可以转化成顶点删除、边折叠及三角形折叠3种已知的几何元素删除算法,因此也可以视做为基于二次误差度量的几何元素删除简化算法的总括算法.最后分别对几种n取值情况列举实验数据,说明该算法的有效性.     

一种任意网格模型的选择细化算法

以基于最大误差L∞控制的网格简化算法为基础,通过删除边操作对网格模型进行向下采样,同时建立各删除操作的依赖关系．在进行网格选择细化时,将细化操作分解为对网格模型的几何修改信息和各细化操作之间的关系信息,确保了网格模型选择细化结果的正确性．实验结果证明了文中算法的有效性．     

基于K-means聚类的三维点云分类

针对三维点云分类算法受到点云稀疏性和无序性影响的问题,提出一种改进算法。在点云预处理阶段,对密集的点云进行冗余数据去除,以减少后续计算量;对于稀疏的点云数据则进行三角形插值计算,以使分类更精确。加入K-means聚类分析算法,之后并行通过PointNet网络进行特征提取,该方法可体现点云空间中的点云分布特性。分别在ModelNet10/40上进行三维点云分类实验,并对比不同[K]值对分类结果的影响。实验结果表明,当[K=5]时分类准确率最高,其在ModelNet10/40上的准确率分别是94.2%和92.6%。提出的算法性能高于其他对比算法,同时训练时间大大减少。     

3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo

A traditional approach to extracting geometric information from a large scene is to compute multiple 3-D depth maps from stereo pairs or direct range finders, and then to merge the 3-D data. However, the resulting merged depth maps may be subject to merging errors if the relative poses between depth maps are not known exactly. In addition, the 3-D data may also have to be resampled before merging, which adds additional complexity and potential sources of errors.This paper provides a means of directly extracting 3-D data covering a very wide field of view, thus by-passing the need for numerous depth map merging. In our work, cylindrical images are first composited from sequences of images taken while the camera is rotated 360° about a vertical axis. By taking such image panoramas at different camera locations, we can recover 3-D data of the scene using a set of simple techniques: feature tracking, an 8-point structure from motion algorithm, and multibaseline stereo. We also investigate the effect of median filtering on the recovered 3-D point distributions, and show the results of our approach applied to both synthetic and real scenes.     

Three-dimensional motion estimation via matrix completion

Three-dimensional motion estimation from multiview video sequences is of vital importance to achieve high-quality dynamic scene reconstruction. In this paper, we propose a new 3-D motion estimation method based on matrix completion. Taking a reconstructed 3-D mesh as the underlying scene representation, this method automatically estimates motions of 3-D objects. A "separating + merging" framework is introduced to multiview 3-D motion estimation. In the separating step, initial motions are first estimated for each view with a neighboring view. Then, in the merging step, the motions obtained by each view are merged together and optimized by low-rank matrix completion method. The most accurate motion estimation for each vertex in the recovered matrix is further selected by three spatiotemporal criteria. Experimental results on data sets with synthetic motions and real motions show that our method can reliably estimate 3-D motions.     

融合快速全局K-means与区域合并的图像分割

提出一种融合快速全局K-means与区域合并的图像分割方法。该方法利用中值滤波方法对图像去噪;运用快速全局K-means算法对图像的颜色空间进行聚类分析;结合区域合并准则,对初始分割合并得到最终的分割结果。实验表明,与同类算法比较,该方法的分割结果在图像细节方面能够很好地满足人的主观视觉。     

Edge enhancement by local deconvolution

In this paper a new approach for blurred image restoration is presented. Our algorithm is based on human vision which zooms back and forth in the image in order to identify global structures or details. Deconvolution parameters are estimated by an edge detection and correspond to the ones of a chosen edge detection model. The segmentation is obtained by merging multiscale information provided by multiscale edge detection. The edge detection is achieved by using a derivative approach following a generalization of Canny-Deriche filtering. This multiscale analysis performs an efficient edge detection in noisy blurred images. The merging leads to the best local representation of edge information across scales. The algorithm deals with a mixed (coarse-to-fine/fine-to-coarse) approach and searches for candidate edge points through the scales. Edge characteristics are estimated by the merging algorithm for the chosen model. Scale, direction and amplitude informations allow a local deconvolution of the original image. The noise problem is not considered in this work since it does not disturb the process. Results show that this method allows non-uniformly blurred image restoration. An implementation of the whole algorithm in an intelligent camera (DSP) has been performed.     

基于信息瓶颈法的图像分离-合并分割算法

在图像处理中,分割算法是其主要研究焦点之一。针对该问题,提出基于信息瓶颈法的新图像分离 合并分割算法。该方法的目的是抽取与输入相关的一个变量的紧密表征,并使得在考虑与输出相关的另一个变量互信息的损失最小。首先,基于一系列图像区域和强度直方图集合之间定义信息渠道,在此渠道中,以互信息的最大化来优化图像分割法;然后,通过最小化互信息损失,完成在上一阶段中获得的多区域合并过程。在二维图像上做的实验表明所提出算法的性能。     

Simultaneous Registration of Multiple Range Views for Use in Reverse Engineering of CAD Models

When reverse engineering a CAD model, it is necessary to integrate information from several views of an object into a common reference frame. Given a rough initial alignment of local 3-D shape data in several images, further refinement is achieved using an improved version of the recently popular Iterative Closest Point algorithm. Improved data correspondence is determined by considering the merging data sets as a whole. A potentially incorrect distance threshold for removing outlier correspondences is not needed as in previous efforts. Incremental pose adjustments are computed simultaneously for all data sets, resulting in a more globally optimal set of transformations. Individual motion updates are computed using force-based optimization, by considering the data sets as implicitly connected by groups of springs. Experiments on both 2-D and 3-D data sets show that convergence is possible even for very rough initial positionings, and that the final registration accuracy typically approaches less than one quarter of the interpoint sampling resolution of the images.     

多颜色空间融合的人体检测算法研究

对运动人体颜色与背景颜色近似时,单个颜色空间不能很好地刻画人体与背景之间的差别这一问题,本文提出一种多颜色空间不同颜色分量融合的策略,即对多个颜色空间的各个分量进行单独的人体检测,统计人体检测性能最好的若干个通道,对这些通道的前景进行融合,得到最终的人体检测结果。另外,为了处理动态背景问题,设计一种基于帧间差分法和定时刷新的背景更新算法。实验结果表明,在复杂场景中,该算法的检测结果要好于基于单颜色空间的人体检测结果,并且该算法能够有效地处理动态背景问题。     

Region based techniques for segmentation of volumetric histo-pathological images

In this article we have presented the application of three region based segmentation techniques namely, seeded volume growing, constrained erosion-dilation techniques and 3-D watershed algorithm. The algorithms are suitably extended to apply on 3-D histo-pathological images. Suitable modifications and extension for each algorithm is done to obtain better segmentation. A quantitative as well as qualitative comparison of the three methods is presented. Modifications to these algorithms for obtaining better results are discussed. The modifications include, (1) design of adaptive similarity measures to control the seeded volume growing and (2) rule-based merging of the over-segmented cells in the case of the 3-D watershed algorithm. Some results and quantitative study is also presented.     

Estimation of 3-D human body posture via co-registration of 3-D human model and sequential stereo information

In this paper, we present a technique for estimating three-dimensional (3-D) human body posture from a set of sequential stereo images. We estimated the pixel displacements of stereo image pairs to reconstruct 3-D information. We modeled the human body with a set of ellipsoids connected by kinematic chains and parameterized with rotational angles at each body joint. To estimate human posture from the 3-D data, we developed a new algorithm based on expectation maximization (EM) with two-step iterations, assigning the 3-D data to different body parts and refining the kinematic parameters to fit the 3-D model to the data. The algorithm is iterated until it converges on the correct posture. Experimental results with synthetic and real data demonstrate that our method is capable of reconstructing 3-D human posture from stereo images. Our method is robust and generic; any useful information for locating the body parts can be integrated into our framework to improve the outcomes.