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在牙齿三维矫正中需要对牙齿进行排列,常用方法是通过人机交互完成,效率不高。提出了一种基于粒子群的自动化排牙方法,将每颗牙齿上的特征点到标准牙弓曲线的距离和作为目标函数,利用粒子群算法对解空间进行搜索,在搜索过程中加入约束条件,得到牙齿移动的最终位置。利用该算法对牙齿进行排列,可以省去人机交互中的平移等操作。实验结果表明该算法能够有效地用于牙齿三维矫正中,提高了排牙效率。 相似文献
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针对虚拟正畸技术中人工排牙效率低下问题,提出一种基于拟合优化的错位牙齿 自动排列方法。对输入的牙列模型建立排牙坐标系,定义单颗牙齿特征点并建立牙齿局部坐标 系。在此基础上,从低维角度分析牙列中各颗牙齿的位置和姿态,采用加权拟合优化的方法分别 计算牙齿的坐标平移量与局部坐标轴旋转量,形成牙齿位姿与空间牙列曲线的关联约束,并结合 矩形包围盒的碰撞检测方法,设计基于最速下降法的迭代算法在空间牙列曲线约束范围内调整牙 齿位姿,完成牙齿的自动排列。实验结果表明,排好的牙列与人工排牙结果相差无异,排牙效率 大大提高;与现有排牙方法相比,更贴近临床牙齿矫治,且牙齿移动代价总量明显降低。 相似文献
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混沌粒子群算法和量子粒子群算法在一定程度上改进了标准粒子群算法的搜索质量,但两者仍存在收敛速度慢、易陷入局部极小等问题。混沌量子粒子群算法将混沌搜索机制引入量子粒子群算法,提高了搜索效率和计算质量。用粒子群算法、混沌粒子群算法、量子粒子群算法和混沌量子粒子群算法对一平板结构进行模型修正,结果表明,混沌量子粒子群算法具有较高的搜索效率和避免陷入局部最优的能力,修正后的模型比单独采用混沌或者量子粒子群算法具有更高的修正精度。 相似文献
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分析了量子行为粒子群优化算法,着重研究了算法中群体粒子的搜索行为,对算法中局部吸引点进行了分析,提出针对粒子在搜索过程中所处的不同搜索环境,将粒子的搜索行为分为四种类型,并能够自适应地学习优化问题环境,采用合适的学习模式,提高算法整体优化性能;将改进后的自学习量子粒子群算法与其他一些改进方法通过CEC2005 benchmark测试函数进行了比较,最后对结果进行了分析,仿真结果显示自学习方法能够显著改善量子粒子群优化算法的性能。 相似文献
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传统的故障识别方法存在准确性低、效率低下的问题。为此,本文提出了一种基于改进粒子群算法的低压配电网故障自动识别方法。利用粒子群算法的全局搜索和优化能力,实现粒子群算法的快速收敛。采集低压配电网的运行信息并进行预处理,获得故障特征向量,并利用改进粒子群算法对特征向量进行优化搜索,实现快速准确的故障识别。此外,通过仿真实验验证了该方法的有效性和性能优势。 相似文献
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为了提高T-S模糊模型的辨识精度和效率,本文提出了一种改进的粒子群算法和模糊C均值聚类算法相结合的模糊辨识新方法。在该方法中,针对粒子群算法在处理高维复杂函数时容易陷入局部极值的问题,提出了一种粒子群局部搜索和全局搜索动态调整的全新优化算法。模糊C均值聚类算法是模糊辨识最常用的方法之一,该算法简单,计算效率高,但是对初始化特别敏感,容易陷入局部最优。为了解决这一问题,利用改进粒子群算法的全局搜索能力优化聚类中心,显著地提高了算法的辨识精度和效率。最后,针对非线性系统进行建模仿真,仿真结果表明了本文方法的有效性和优越性。 相似文献
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针对基本粒子群(PSO)算法不能较好地解决旅行商优化问题(TSP),分析了基本粒子群算法的优化机理,在新定义粒子群进化方程中进化算子的基础上利用混沌运动的随机性、遍历性等特点,提出一种结合混沌优化和粒子群算法的改进混沌粒子群算法.该算法对惯性权重进行自适应调整,引入混沌载波调整搜索策略避免陷入局部最优,形成一种同时满足全局和局部寻优搜索的混合离散粒子群算法,使其适合解决TSP此类组合优化问题.利用MATLAB对其进行了仿真.仿真结果说明此算法的搜索精度、收敛速度及优化效率均较优,证明了此算法在TSP中应用的有效性,且为求解TSP提供了一种参考方法. 相似文献
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在光学非接触三维测量中,复杂对象的重构需要多组测量数据的配准。最近点迭代(ICP)算法是三维激光扫描数据处理中点云数据配准的一种经典的数学方法,为了获得更好的配准结果,在ICP算法的基础之上,提出了结合基于特征点的等曲率预配准方法和邻近搜索ICP改进算法的精细配准,自动进行点云数据配准的算法,经对牙齿点云模型实验发现,点云数据量越大,算法的配准速度优势越明显,采用ICP算法的运行时间(194.58 s)远大于本算法的运行时间(89.13 s)。应用实例表明:该算法具有速度快、精度高的特点,算法效果良好。 相似文献
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Image registration is a crucial progress in detecting oil spilled on the sea and is also important for estimating the volume of the oil spill, especially when one image cannot cover the entire polluted region. In this article, a new algorithm is proposed to register geometrically distorted aerial images of oil spill accurately and automatically. There are two stages in this algorithm: coarse registration and fine registration. Invariants-based similarity and relative space distance are applied to coarse matching. Then improved iterative closest point (ICP) algorithm is used for registering images finely, which is the combination of ICP and a method of solving assignment problem to deal with mismatches. The performance of the proposed algorithm is evaluated by registering oil spill ultraviolet (UV) and infrared (IR) images, respectively. Compared with traditional ICP and other algorithms, the efficiency and accuracy of the proposed algorithm are highly improved. 相似文献
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在对特征辨识度低的点云进行配准的过程中,传统的基于局部特征提取和匹配的方法通常精度不高,而基于全局特征匹配的方法精度和效率也难以保证。针对这一问题,提出一种改进的局部特征配准方法。在初步配准阶段,设计了一种基于法向量投影协方差分析的关键点提取方法,结合快速特征直方图(FPFH)对关键点进行特征描述,定义多重匹配条件对特征点进行筛选,最后将对应点的最近距离之和作为优化目标进行粗匹配;在精配准阶段,采用以点到平面的最小距离作为迭代优化对象的改进迭代最近点(ICP)算法进行精确配准。实验结果表明,在配准特征辨识度低的点云时,相较于其他三种配准方法,该方法能保持高配准精度的同时降低配准时间。 相似文献
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Dinggang Shen Author Vitae 《Pattern recognition》2007,40(4):1161-1172
We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images. 相似文献
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ZHANG Hongbin & XIE Feng Computer Institute of Beijing University of Technology Beijing China 《中国科学F辑(英文版)》2005,48(2):179-191
1 Introduction In recent years, there has been growing interest in the range sensing techniques for building 3D computer models of real-world objects and scenes without requiring hu-mans to manually produce these models using laborious and error-prone CAD-based approaches. Using range sensors, users are able to capture 3D range images of objects from different viewpoints that may be combined to form the final model of the object or scene[1]. These models then may be used for a variety of app… 相似文献
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Fei Wang Baba C Vemuri Anand Rangarajan Stephan J Eisenschenk 《IEEE transactions on pattern analysis and machine intelligence》2008,30(11):2011-2022
Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data. 相似文献
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We identify a novel parameterization for the group of finite rotations (SO3), consisting of an atlas of exponential maps defined over local tangent planes, for the purpose of computing isometric transformations in registration problems that arise in machine vision applications. Together with a simple representation for translations, the resulting system of coordinates for rigid body motions is proper, free from singularities, is unrestricted in the magnitude of motions that can be represented and poses no difficulties in computer implementations despite their multi‐chart nature. Crucially, such a parameterization helps to admit varied types of data sets, to adopt data‐dependent error functionals for registration, seamlessly bridges correspondence and pose calculations, and inspires systematic variational procedures for computing optimal solutions. As a representative problem, we consider that of registering point clouds onto implicit surfaces without introducing any discretization of the latter. We derive coordinate‐free stationarity conditions, compute consistent linearizations, provide algorithms to compute optimal solutions and examine their performance with detailed examples. The algorithm generalizes naturally to registering curves and surfaces onto implicit manifolds, is directly adaptable to handle the familiar problem of pairwise registration of point clouds and allows for incorporating scale factors during registration. 相似文献