共查询到19条相似文献，搜索用时 31 毫秒
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物体变形的广义形态变换方法 总被引：3，自引：2，他引：3
将广义形态变换理论用于非刚体运动的描述和内插,通过对非刚体的凸剖分把非刚体的运动分解为非刚体的变形与子凸集的旋转,提出物体近似骨架的概念;通过近似骨架实现子凸集匹配,实现了任意非同拓扑结构(包括有孔及凹多面体)物体的变形．广义形态变换具有基于体元的变形方法和基于边界形状的变形方法的优点,同时克服了它们的缺点．实验表明,该方法变形物体边界光滑、定位精度高、计算速度快,可应用于CAD、虚拟现实和生物医学工程。 相似文献
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一种基于广义形态变换的采样方法 总被引：1，自引：0，他引：1
提出了一种基于集合势的广义形态变换形式。该变换能反映结构元素作用于目标对象的强度,控制对目标对象的过度膨胀或过度腐蚀。在广义形态变换性质研究的基础上所提出的广义形态采样方法,能更好地保留图像的形态信息和边缘信息。测试结果表明,基于广义形态变换的采样方法在提高形态变换效率及保持图像细节信息方面相对于现有的采样方法都有一定程度的改善。 相似文献
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现有的非刚体三维运动重建算法很难根据不同的场景、不同的非刚体来寻找不同的形状基,这种情况对重建过程产生了很大的影响,造成模型失真。为了解决这一问题,提出一种基于Murkowski距离连续的非刚体三维运动恢复算法,该算法根据在高速分解的图像序列中帧与帧之间的运动参数与特征点位移变化都呈现连续平缓的物理特性,在Murkowski距离约束的情况下,将运动结构参数通过非线性优化的方法来进行求解,最终得到非刚体的三维运动结构,并且通过仿真实验,验证了它的可行。 相似文献
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主要介绍对边界像素进行约束来恢复画面中块的方法。这种方法基本假设前提是如果对块中丢失和它的边界像素进行DCT变换,含高频成分系数归化为零,这样就可以得到一系列线性方程,通过对这些方程求解就可以求出丢失的像素的近似值。这种方法只需对八个边界像素进行运算。 相似文献
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主要介绍对边界像素进行约束来恢复画面中块的方法.这种方法基本假设前提是如果对块中丢失和它的边界像素进行DCT变换,含高频成分系数归化为零,这样就可以得到一系列线性方程,通过对这些方程求解就可以求出丢失的像素的近似值.这种方法只需对八个边界像素进行运算. 相似文献
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通过将最大相关原则一般化为部分相关原则的方法对形态变换进行了扩展和推广,使得新的形态变换形式能够反映出结构元素B与图像x各种不同的相关程度;通过对其基本性质的研究和建立,提出了具有调节形态变换相关强度的广义形态变换理论;通过引入相关强度系数的方法可以实现对结构元素与图像之间的相关程度以及结构元素对图像变换的敏感度的控制,同时能调整并控制由于不同尺度的结构元素所产生的过度膨胀或腐蚀现象。 相似文献
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提出了一种非刚体图象序列特征点对应的新方法,首先根据图象序列帧间的时间间隔很小这一特点,指出特征点在图象帧间的运动具有平滑性,进而提出了一种特征点运动平滑的代价函数。然后将目标跟踪技术应用于特征点的跟踪,利用两个一维卡尔曼滤波器分别对特征点的两个坐标进行预测跟踪。 相似文献
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An approach of morphing by decomposing two objects into sets of individual convex sub objects respectively and contructing the mapping between two sets is presented The Minkvski addition of two convex sub-objects according to their mapping ralationship is calculated, then all the Minkovski additions are combined to obtain the final result. Nonrigid body motion can be divided into nonrigid body metamorphosis and rigid body rotation A novel method for describing nonrigid body motion based on generalized morph-translation is proposed. This method can solve the metamorphosis problem of two non-homotopic objects. Experiments show tha this method can generate natural, high quality metamorphosis results with simple computation. This method can also be used in interpolation between two keyframes in 2D and 3D computer animation automatically. 相似文献
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一种全局优化的多边形变形方法及应用 总被引：10，自引：1，他引：10
通过对多边形的凸部分,并建立２种不同多边形的凸子集映射,提出了一种全新的基于凸多边形的全局优化方法,解决了任意非同拓扑结构（包括有孔及凸边形）的变形问题。理论上证明了此方法的正确性,讨论了不同凸剖分对变形的影响。实验证明此方法变形效果自然、质量好、速度快、自动化程度高,并可用于汉字的合成与二维动画关键帧的内插。 相似文献
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文中提出了一种全新的基于凸多边形变形的汉字字形自动生成方法.通过对字体的凸剖分,把字体分解成若干个子凸多边形,并经过全局优化迭代建立两种不同字体的子凸多边形间的匹配关系,计算两匹配子凸多边形的形态和,再进行合并,则得到合成后的字体.理论上证明了此方法的正确性,讨论了不同剖分对生成字形的影响,可解决不同构字体间的合成问题,通过不同的结构元的形态变换,实现不同字体的衍生.实验证明此方法生成的汉字字形多,质量好,自动化程度高,并可用于三维字体生成. 相似文献
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This paper investigates a general monotropic optimization problem for continuous‐time networks, where the global objective function is a sum of local objective functions that are only known to individual agent, and general constraints are taken into account, including local inequality constraints, global equality constraint, and local feasible constraints. In addition, all functions involved in the objective functions and inequality constraints are not necessarily differentiable. To solve the problem, a distributed continuous‐time algorithm is designed using subgradient projections, and it is shown that the proposed algorithm is well defined in the sense that the existence of its solutions can be guaranteed. Furthermore, it is proved that the algorithm converges to an optimal solution for the general monotropic optimization problem. Finally, a simulation example is provided for validating the theoretical result. 相似文献
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研究采用均匀 B-样条建立了插值区间型数据的鲁棒优化模型,与以传统多项式样 条为样条函数的鲁棒优化模型相比,存在表达式更为简单、计算过程更加容易等优势。该模型 是易解的有限凸优化问题,而传统多项式模型需要通过复杂变化,才能将带有无限个约束的凸 优化问题转化为有限优化问题。为增加模型的自由度,即插值曲线的可调性,首先讨论如何基 于给定的区间型数据扩建出建模过程中需要的全部特征多边形顶点的方法,之后具体采用工程 中经常使用的二次和三次均匀 B-样条,建立了适用于现有优化算法和软件的鲁棒优化模型,数 值实验部分证明了以上模型的易解性和有效性。 相似文献
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Lijian Chen 《Optimization methods & software》2019,34(1):115-138
We proposed a polynomial approximation-based approach to solve a specific type of chance-constrained optimization problem that can be equivalently transformed into a convex programme. This type of chance-constrained optimization is in great needs of many applications and most solution techniques are problem-specific. Our key contribution is to provide an all-purpose solution approach through Monte Carlo and establish the linkage between our obtained optimal solution with the true optimal solution. Our approach performs well because: First, our method controls approximation errors for both the function value and its gradient (or subgradient) at the same time. This is the primary advantage of our method in comparison to the commonly used finite difference method. Second, the approximation error is well bounded in our method and, with a properly chosen algorithm, the total computational complexity will be polynomial. We also address issues associated with Monte Carlo, such as discontinuity and nondifferentiability of the function. Thanks to fast-advancing computer hardware, our method would be increasingly appealing to businesses, including small businesses. We present the numerical results to show that our method with Monte Carlo will yield high-quality, timely, and stable solutions. 相似文献
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《国际计算机数学杂志》2012,89(13):2838-2851
In this paper, we investigate the difference of Shepard's generalized operators S σ from the approximated set of data for various weight functions σ. Bounds are given for the sizes of the ‘bumps’ shown on the graph of S σ for σ(d)=1/d in dimension N=1, and the best weight function σ for practical use is proposed. 相似文献
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《Optimization methods & software》2012,27(1):71-88
In a distributed optimization problem, the complete problem information is not available at a single location but is rather distributed among different agents in a multi-agent system. In the problems studied in the literature, each agent has an objective function and the network goal is to minimize the sum of the agents’ objective functions over a constraint set that is globally known. In this paper, we study a generalization of the above distributed optimization problem. In particular, the network objective is to minimize a function of the sum of the individual objective functions over the constraint set. The ‘outer’ function and the constraint set are known to all the agents. We discuss an algorithm and prove its convergence, and then discuss extensions to more general and complex distributed optimization problems. We provide a motivation for our algorithms through the example of distributed regression of distributed data. 相似文献