基于数字距离变换的3D模型骨架提取算法

在获得三维模型体素表示的基础上 ,通过比较模型体素及其 2 6连通域体素到模型轮廓的最小欧式距离 ,提出了一种利用骨架体素 2 6连通域的对称性进行三维模型骨架体素提取的算法 整个算法只需遍历一次体数据集即可自动完成模型骨架的提取过程 .实验表明 ,该算法具有较高的效率和精度 .     

高斯曲率约束的MRG骨架提取优化算法

三维模型的骨架保持了模型的拓扑特性,并被广泛应用于模型相似性比较、计算机动画及压缩等领域.根据多分辨率Reeb图的原理,提出了一种基于离散高斯曲率约束的骨架提取优化算法.通过计算网格顶点的离散高斯曲牢判断曲面局部凸凹特性,以获取模型表面的双曲极值点作为约束点;并依据约束点及其邻域的μ函数值产生的分裂线进行区域细分,获得子连通区域、确定关节点、形成优化的骨架结构.实验结果表明,该算法有效地突出了模型的拓扑分支特征以及模型表面的细节,提高了骨架提取的精度和效率.     

基于3D模型的骨架提取

根据3D模型骨架的基本定义,即内切圆圆心集合的定义,采用了一种通过对网格化的3D模型体素化的方法,运用改进的欧式距离变换提取出3D模型的骨架点,通过对骨架点的保存连通的无损连接形成了模型的骨架.对于提取的模型骨架具有的性质,提出了基于骨架的医学内窥镜漫游、基于骨架的局部网格重建(模型动画初步)、基于骨架的模型分割.为了方便应用这些功能,制作了一个友好的用户使用界面,使其功能得以更好的应用.     

空间球拓扑外联的岩心线骨架提取方法

在分析现有骨架提取算法的基础上,针对现有的岩心骨架提取方法中存在的效率较低、骨架连通性差等问题,提出了一种基于空间球外联的岩心线骨架提取的几何方法.该方法以CT扫描图像为基础进行模型重构,并用Delaunay算法剖分得到四面体实体模型.依据岩心结构的四面体模型的特点,通过寻找模型四面体的外接球之间的拓扑邻接关系,到岩心的骨架结构.通过等效计算,简化连接,得到单一的线骨架结构,保证了骨架结构与岩心结构的拓扑一致性.实验结果表明,文中方法可以保证骨架的连通结构,且不受模型边界质量的影响,适用于复杂的岩心形体结构,实现了岩心骨架的提取.     

基于截线法的快速骨架提取算法

提出了一种快速的骨架提取算法.该方法首先在轮廓离散曲线演化的基础上,根据显著凸顶点的类型将轮廓多边形进行分块,得到一个主分支轮廓和多个水平分支轮廓;然后分别利用水平截线法和垂直截线法提取骨架的主分支和水平分支;最后将水平分支拼接在主分支上,得到完整的骨架.实验结果表明,该骨架提取算法可以得到连通的骨架,并在Kimia数据集上取得了较好的效果.此外,算法在自然图像上的效果也很好,尤其适用于视频中的行人骨架提取.与经典骨架提取算法相比,该算法的时间复杂度较低,可以满足实时处理的要求.     

基于非脊点下降算子的多尺度骨架化算法

骨架是目标表示的一种重要方式.提出了一种基于区域标记直接从灰度图像中提取的骨架的新算法.算法对脊点概念作了补充撰述,组合利用了目标的轮廓与区域信息,采用了层次化的处理策略,适用于稳健地提取规则和不规则目标完整的多尺度骨架.所提取的骨架彼此连通、单像素宽并与原始图像拓扑一致.将算法应用于实际图像,检测到了与人视觉感知相一致的目标骨架.     

骨架驱动的三维模型变形方法

为发展三维网格模型的变形技术,研究了多种三维模型变形算法,通过对骨架驱动变形算法的深入研究,针对现行算法多是以单一骨架驱动变形的不足,提出了一种新的基于多骨架点驱动的交互式局部变形方法.有效结合模型的骨架图结构,确定各骨架点对应的局部区域.并将骨架点拟合为二次Bézier曲线,通过交互式拖动任意骨架点计算与之相连的多骨架点的动态变化,实现模型局部区域的自然形变.实验结果表明了该算法的有效性和直观性.     

无线传感器网络三维表面k覆盖多连通部署方法*

无线传感器网络中覆盖连通问题是基本且重要的问题,三维表面作为无线传感器网络中的一种特殊情形,对应于现实世界中的山体,为了解决这类与实际应用密切相关的问题,提出了三维表面k覆盖多连通部署方法。该方法结合三维表面的地形特征,首先在目标区域自由选择网格大小进行划分,接着在各网格之间建立多连通关系,再通过方向梯度概率感知模型在网格内先构造k覆盖集,然后利用最小生成树算法构造连通图,最后找出关节点构造双连通图。大量仿真实验表明,该方法能够对目标区域进行完全覆盖和连通,并且能保证网络的健壮性。     

基于骨架的网格模型变形

现有骨架驱动变形算法多以单一骨架驱动变形,且骨架预设十分复杂。为此,提出一种基于骨架的网格模型变形算法。结合多分辨率Reeb图方法提取模型的骨架结构,确定各骨架点对应的局部区域,将骨架点插值构造二次Bézier曲线,通过交互式拖动任意骨架点,计算与其相连多骨架点的动态变化情况,实现模型局部区域的自然形变。实验结果表明,该算法能获得较为自然平滑的变形结果。     

基于α-shape的树冠LOD建模

以点云形式的树模型为输入,以3D Delaunay三角化为基础,选取最优α值并生成点云的最小连通α-shape;然后计算其距离场并进行滤波处理,以提取α-shape的光滑等距面;进一步采用Laplacian骨架提取的分割方法从等距面中分割出树干和树冠点云的包围体。最后以树冠包围体内点云的多分辨率α-shape为细节层次(LOD)模型用于非真实感VR场景。     

Skeleton-enhanced line drawings for 3D models

We present a novel line drawing approach for 3D models by introducing their skeleton information into the rendering process. Based on the silhouettes of the input 3D models, we first extract feature lines in geometric regions by utilizing their curvature, torsion and view-dependent information. Then, the skeletons of the models are extracted by our newly developed skeleton extraction algorithm. After that, we draw the skeleton-guided lines from non-geometric regions through the skeleton information. These lines are combined with the feature lines to render the final line drawing result using the line optimization. Experimental results show that our algorithm can render line drawings more effectively with enhanced skeletons. The resulting artistic effects can capture the local geometries as well as the global skeletons of the input 3D models.     

Curve-skeleton extraction using iterative least squares optimization

A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the pre-computational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques.     

Recognition of handwritten cursive Arabic characters

An automatic off-line character recognition system for handwritten cursive Arabic characters is presented. A robust noise-independent algorithm is developed that yields skeletons that reflect the structural relationships of the character components. The character skeleton is converted to a tree structure suitable for recognition. A set of fuzzy constrained character graph models (FCCGM's), which tolerate large variability in writing, is designed. These models are graphs, with fuzzily labeled arcs used as prototypes for the characters. A set of rules is applied in sequence to match a character tree to an FCCGM. Arabic handwritings of four writers were used in the learning and testing stages. The system proved to be powerful in tolerance to variable writing, speed, and recognition rate     

Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition

Applied Intelligence - In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have...     

Motor Control Techniques for Figure Animation

Computer models of skeletons may not show muscle tissue or clothing, but they are an excellent means of displaying the fundamental properties of human motion.     

一种基于算法骨架的函数式程序变换技术

如何提高函数式程序设计语言在传统冯·诺依曼机器上的执行速度.及效率,一直是该领域中研究的主要论题,对此,并行图归约技术、并行闭包归约、并行编译、并行程序转换等等技术相继成为改善这种状况的措施。     

Real-time hand detection using continuous skeletons

In this paper, a fast and reliable method for hand detection based on continuous skeletons approach is presented. It demonstrates real-time working speed and high detection accuracy (3–5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). These make it suitable for use as a part of modern hand identification systems including mobile ones. Overall, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g., interactive segmentation with seeds or abounding box).     

The Log Skeleton Visualizer in ProM 6.9

Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest.