一种用于“蛇形”机动目标的跟踪方法研究

为了满足光电跟踪系统中对“蛇形”机动目标跟踪的要求,针对以前应用的Singer模型的缺陷,根据“蛇形”机动目标运动规律,提出一种机动目标转弯模型,结合扩展卡尔曼滤波算法,对典型“蛇形”机动目标进行跟踪。通过Monte Carlo仿真,实现了在平面内对“蛇形”机动目标位置的准确跟踪,并且距离均方根误差（RMSE）比Singer模型要小很多,从而验证了该方法的有效性。     

Robin型离散Schwarz波形松弛算法的收敛性分析

Schwarz波形松弛(Schwarz waveform relaxation,SWR)是一种新型区域分解算法,是当今并行计算研究领域的焦点之一,但针对该算法的收敛性分析基本上都停留在时空连续层面.从实际计算角度看,分析离散SWR算法的收敛性更重要.本文考虑SWR研究领域中非常流行的Robin型人工边界条件,分析时空离散参数t和x、模型参数等因素对算法收敛速度的影响.Robin型人工边界条件中含有一个自由参数p,可以用来优化算法的收敛速度,但最优参数的选取却需要求解一个非常复杂的极小-极大问题.本文对该极小-极大问题进行深入分析,给出最优参数的计算方法.本文给出的数值实验结果表明所获最优参数具有以下优点:(1)相比连续情形下所获最优参数,利用离散情形下获得的参数可以进一步提高Robin型SWR算法在实际计算中的收敛速度,当固定t或x而令另一个趋于零时,利用离散情形下所获参数可以使算法的收敛速度具有鲁棒性(即收敛速度不随离散参数的减小而持续变慢).(2)相比连续情形下所获收敛速度估计,离散情形下获得的收敛速度估计可以更加准确地预测算法的实际收敛速度.     

ASIS�㷨�Ƿ�Ӧ�ù㷺����?

本文将辅助--充分交织策略, 即Yu和Meng (2011)中提到的ASIS算法, 应用于Gibbs抽样算法中以提高两个方差参数的收敛性. 我们通过对潜在规模缩减因子(PSRF)、轨迹图及后验估计 比较了ASIS算法与普通Gibbs抽样算法的性能, 其中一个参数的收敛性有了很大的提高, 但另一个参数没有很明显的提高. 然而, 由于ASIS算法相与普通的Gibbs抽样算法相比极大地减少了为达到收敛所 需要的循环次数, 整体的抽样性能得到了极大的提高.     

ASIS算法是否应该广泛采用?(英文)

本文将辅助-充分交织策略,即Yu和Meng(2011)中提到的ASIS算法,应用于Gibbs抽样算法中以提高两个方差参数的收敛性.我们通过对潜在规模缩减因子(PSRF)、轨迹图及后验估计比较了ASIS算法与普通Gibbs抽样算法的性能,其中一个参数的收敛性有了很大的提高,但另一个参数没有很明显的提高.然而,由于ASIS算法相与普通的Gibbs抽样算法相比极大地减少了为达到收敛所需要的循环次数,整体的抽样性能得到了极大的提高.     

异步并行矩阵多分裂多参数松弛算法

通过改进与推广Bru,Elsner和Neumann的异步算法模型,文[2]设计了一类适用于MIMD系统的异步并行多分裂松弛算法。该算法模型具计算方便,通讯灵活,自由等诸多良好的特点。 更为一般地,基于矩阵多分裂的概念,我们在本文中提出了一类异步并行多分裂多参数松弛算法。它既以[2]中的异步并行多分裂AOR算法等做为特例,且随着松弛参数的不同     

基于软集合参数约简的一种启发式算法

在比较和分析了原有软集合参数约简文献的基础上,本文总结出了一些参数约简的结论,并结合参数约简的思想,提出了一种参数约简的启发式算法.同时,本文给出了该启发式算法的具体算法过程及算例,并对该算法的相关理论进行了证明.最后,将该算法的约简结果与其它两种算法进行对比分析,表明本文算法的高效性和优越性.     

一种新的二次约束二次规划问题的分支定界算法

本文为了获得二次约束二次规划(QCQP)问题的全局最优解,提出一种新的参数化线性松弛分支定界算法.该算法利用参数化线性松弛技术,得到(QCQP)的全局最小值的下界,并利用区域缩减技术以最大限度地删除不可行区域,加快该算法的收敛速度.数值实验表明,本文提出的算法是有效并且可行的.     

一类新的具有充分下降条件和强收敛性的共轭梯度法

本文研究了求解无约束优化问题的WYL共轭梯度法.利用修正迭代格式,得到了算法在每步迭代能产生不依赖于搜索条件的充分下降方向.同时,在原算法中关于Wolfe条件中参数去掉的情况下,获得了本文算法是强收敛的.数值实验说明本文算法可以有效求解测试问题.     

一类新的具有充分下降条件和强收敛性的共轭梯度法（英文）

本文研究了求解无约束优化问题的WYL共轭梯度法.利用修正迭代格式,得到了算法在每步迭代能产生不依赖于搜索条件的充分下降方向.同时,在原算法中关于Wolfe条件中参数去掉的情况下,获得了本文算法是强收敛的.数值实验说明本文算法可以有效求解测试问题.     

精确解析法的子结构算法

文[1]提出精确解析法,用以求解任意变系数常微分方程,并利用初参数算法给出一个解的解析表达式.但利用初参数算法,对某一类问题,如长柱壳弯曲和振动等,它们的解将难以在计算机上得到.本文通过非均匀轴对称长圆柱壳弯曲问题,给出精确解析法的子结构算法,它能够计算初参数算法在计算机上不能解决的问题.问题最后和初参数算法一样能归结为求解一个低阶代数方程组.文末给出算例,表明本文算法的正确性,并和初参数算法作了比较.     

一种无重新初始化的水平集方法的错觉轮廓捕捉新方法

在错觉轮廓捕捉模型建立前,我们要得到根据物体边界的符号距离函数时,用Eikonal方程不能实现的,我们用基于水平集方法的分割技术实现,扩大了模型的使用范围;在Zhu和Chan等人的错觉轮廓捕捉模型基础上引入了李纯明等人提出的符号距离约束信息,这就使得在水平集函数演化时不必对其重新初始化,并大大简化了模型的数值处理水平集函数的演化速度.并通过实验验证了该方法的优势.     

Adaptive Segmentation Model for Images with Intensity Inhomogeneity based on Local Neighborhood Contrast

Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing, especially in medical image processing and analysis. Some local region-based models work well on handling intensity inhomogeneity, but they are always sensitive to contour initialization and high noise. In this paper, we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential equation. Firstly, a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information, respectively. Secondly, a tradeoff function is defined to adjust adaptively the weight between two fitting terms, which is based on the neighborhood contrast of image pixel. Finally, a weighted regularization term related to local entropy is used to ensure the smoothness of evolution curve. Meanwhile, a distance regularization term is added for stable level set evolution. Experimental results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively, which thereby avoiding the influence of contour initialization on segmentation results. Besides, the proposed model works better on noise images comparing with two relevant segmentation models.     

Image segmentation by level set evolution with region consistency constraint

Image segmentation is a key and fundamental problem in image processing, computer graphics, and computer vision. Level set based method for image segmentation is used widely for its topology flexibility and proper mathematical formulation. However, poor performance of existing level set models on noisy images and weak boundary limit its application in image segmentation. In this paper, we present a region consistency constraint term to measure the regional consistency on both sides of the boundary, this term defines the boundary of the image within a range, and hence increases the stability of the level set model. The term can make existing level set models significantly improve the efficiency of the algorithms on segmenting images with noise and weak boundary. Furthermore, this constraint term can make edge-based level set model overcome the defect of sensitivity to the initial contour. The experimental results show that our algorithm is efficient for image segmentation and outperform the existing state-of-art methods regarding images with noise and weak boundary.     

基于EM算法和小波多尺度边缘检测的活动轮廓模型初始化

本文研究了活动轮廓模型初始化的问题.利用EM算法和小波多尺度边缘检测的方法,获得了活动轮廓模型的外部能量函数和初始曲线,并对于具体的X射线图像给出了实验结果和该算法的有效性.     

An explicit formula for a Bennequin-type invariant of apparent contours

We derive an explicit formula for the computation of a Vassiliev-type invariant of an apparent contour introduced recently by Ohmoto and Aicardi using appropriate linking numbers of its Legendrian lift. Our formula does not require the construction of the Legendrian lift and only takes into account the nodes, the cusps, the extremal points and the orientation of the apparent contour. In this way the computation can be implemented into a computer program; we demonstrate its use with some examples.     

Robust Contour Tracking Model Using a Variational Level-Set Algorithm

In this work, we provide a novel variational level-set based object contour tracking approach. Thus, a mathematically rigorous variant of the Chan-Vese algorithm for image segmentation via geometric active contour model is proposed here. With respect to the original contour detection algorithm, the level set function ?(t) defining the evolving contour S _t  = {x; ?(t, x) = 0} is iteratively computed from a nonlinear parabolic boundary value problem that is well posed in the space of functions with bounded variations. We provide a robust mathematical justification of the proposed level-set model.     

Optimization of image quality for decoded images using three-dimensional smoothing method

Joint Photographic Experts Group (JPEG) baseline algorithm is widely used because of its high compression capability. The algorithm has a characteristic that the degradation of image quality tends to be perceived as the compression ratio becomes high. The eyesore degradations are false contour and mosquito. A method of improving the image quality is proposed. The first step: a domain of false contours is extracted from the image and the second step: the domain is smoothed by a fitting process. It is confirmed that this method is effective in improving the image quality degraded by the JPEG high compression.     

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

Segmenting the lesion regions from the ultrasound (US) images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound (HIFU), as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons: (1) the blurry or missing boundaries of lesion regions in the HIFU images and (2) the deformation of uterine fibroids caused by the patient’s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier (ALM). Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.     

Swarm contours: A fast self‐organization approach for snake initialization

A major obstacle in real‐time performance of a visual tracking system is its initialization phase. Inspired by social behavior in fish and ant groups, a fast self‐organization approach to active‐contour initialization is proposed. Contours are emerged during two separate phases of aggregation and self‐assembly. Aggregation is achieved by a superposition of simpler behaviors, hunting, avoidance, and opportunism. Self‐assembly, which constitutes the explicit contour formation, occurs by mating behavior when the swarm becomes quite stable. Experiments indicate that the proposed method outperforms exhaustive image search for finding contours in high resolutions. © 2006 Wiley Periodicals, Inc. Complexity 12: 41–52, 2006