基于局部参数运动模型的人脸表情特征提取

提出了一种基于局部参数运动模型提取人脸表情运动参数的方法.对图像序列进行预处理,提取出能够体现表情的五官特征;用块匹配法跟踪了特征区域的关键点;基于特征点匹配结果和局部参数运动模型,建立特征区域的运动方程;通过非线性约束优化方法估计局部运动参数.实验结果表明,该方法可行有效.     

时空联合的红外运动目标提取算法

针对红外图像对比度差、边缘模糊的特点,提出了一种基于时空联合的红外序列图像目标提取的新方法.算法充分利用了红外目标的亮度特征、背景信息以及运动信息.时域分割中通过建立帧差图像背景的高斯分布模型,采用变化检测模板来确定红外目标约束区域.然后,构造图像像素与区域之间的空间关系隶属度矩阵并约束到传统的模糊聚类算法中,空域分割则利用该模糊聚类来对目标约束区域进行有效分割.最后将时空分割结果融合便能实现最终的红外目标提取.实验结果表明,该方法简单有效,能准确提取动态场景中的红外目标.     

基于脊线预配准的肋骨骨折钢板预弯算法研究

提出了一种可以在肋骨骨折手术前获取目标固定钢板几何参数的仿真方法.其主要操作步骤包括:通过CT扫描获取骨折部位的序列断层图像,利用Curvelet变换对CT序列图像进行去噪处理并建立三维模型;根据肋骨的形状特征,提取两段断骨的脊线进行空间预配准;并驱动两段断骨进行模型预配准.使用网格模型内三角面片的面积特征分割得到两个...     

基于snake模型的IVUS图像序列三维分割方法

针对血管内超声（Intravascular Ultrasound,IVUS）图像序列中血管壁内外膜轮廓的提取问题,提出一种基于snake模型的三维并行分割方法。首先,对原始图像进行滤除噪声和抑制环晕伪像等预处理。然后,获取IVUS图像序列的四个纵向视图,并从中提取出内腔边界和中-外膜边界。通过将这些边界曲线映射到各帧IVUS图像中,得到横向视图中的初始轮廓。最后,将该初始轮廓作为snake模型的初始形状,通过使snake能量函数最小,模型不断变形,最终得到各帧IVUS图像中的内腔和中-外膜边界。该方法可实现对IVUS图像序列的并行分割,与二维串行分割方法相比,可大大提高处理效率。采用大量临床图像数据的实验结果证明该方法可自动、快速、可靠的完成IVUS图像序列的分割。     

单层空间网格结构刚性节点优化设计方法研究

单层网格结构中的节点不仅须传递轴力，也须传递两个方向上的弯矩。因此，单层网格结构中的节点为受力复杂的刚性节点，应具有合理的结构形式和良好的力学性能。同时，单层网格结构节点连接复杂、高空施工难度大，节点与杆件应有可靠、方便、通用的连接方式。该文提出了单层网格结构刚性节点优化设计方法:通过构造设计节点端，实现节点与杆件的连接；通过对受力复杂的节点核进行拓扑优化，开发高效的受力节点形式。在优化模型中，通过与外荷载无关的自平衡力系表示节点弯曲刚度，使得优化后的节点在任意方向上均有良好的弯矩传递能力。为避免拓扑优化中将力直接施加在设计域而导致的应力奇异问题及节点形式受限的缺点，通过施加加速度场，以体积惯性力等效集中荷载，降低了优化结果对边界条件的敏感性，获得了稳定、可靠的优化结果，解决了刚性节点拓扑优化中的关键技术难点。并以一个实际结构中的节点为例进行优化设计。通过分析节点端连接性能、力学性能以及与传统节点进行对比，验证了将拓扑优化和构造设计相结合的刚性节点优化设计方法。     

基于空间拓扑关系的目标自动跟踪与位姿测量技术

针对试飞光电测试中目标运动轨迹姿态自动化测量的问题,提出一种基于空间拓扑关系的目标自动识别跟踪与位姿参数同步测量技术。首先通过对被测目标表面的圆形标志点密集布设、目标运动过程的图像高速采集、圆形标志点不同视角下的自动精确提取,实现目标的自动检测;之后基于多点空间拓扑关系进行同名像对的匹配,并结合双摄像机的标定参数进行前方交会测量,实现目标表面标志点的三维重建;再采用基于多点空间拓扑约束、点距判定以及坐标旋转平移变换算法的点云块配准方法,实现目标自动跟踪与运动过程的轨迹姿态参数测量。试验证明:该方法切实可行,可获得亚毫米级的定位精度和优于0.11°的定姿精度,且能够全自动处理,从而大大提升试飞测试中影像测量的数据处理效率,同时可为飞行影像数据自动化、实时化处理提供一种有效技术手段。     

一种基于印刷图像处理技术的模糊图像复原方法

根据印刷图像处理技术的基本原理,针对印前的电子出版图像处理中存在的原稿图像运动模糊现象,介绍了约束最小二乘方滤波复原技术消除图像模糊方法.阐述了运动模糊图像的退化模型和复原模型,利用二值图的信息来估计模糊方向,通过对二值图的矩阵变换来估计模糊尺度,再利用约束最小二乘方滤波的方法复原图像.实验表明,该方法行之有效.     

变形模型技术在冠状动脉造影图像序列后处理中的应用

以冠状动脉血管轴线的三维运动跟踪和心脏的形状建模为例,论述了变形模型在X射线心血管造影图像序列后处理中的应用.首先采用3D snake模型,表示血管轴线的曲线在三维空间中的变形,完成对血管骨架的序列重建.然后以各时刻的血管骨架点作为采样点,采用基于扩展超二次曲面的变形模型,建立心脏形状模型,通过分析不同时刻模型参数的变化,获得心脏变形参数的估计.     

基于模板变形的三维人体头面部测点识别

目的人体测量项目是由测点定义的,在三维非接触人体测量中,测点识别是人体测量的关键。为快速有效地实现头面部测点的识别,本文研究提出了一种基于模板变形的三维人体头面部测点自动识别方法。方法建立了一个标注了50个测点的三维人体头面部三角网格模型作为模板网格,每个被测者的三维头面部扫描模型作为目标模型。首先通过自动查找模板网格和目标模型上对应的14个特征点作为约束进行初始变形,将模板网格变形为接近目标模型的最小二乘网格;然后进行精细配准,即在目标模型上不断增加与模板网格的顶点相对应的点进行变形配准。经过迭代计算,最终将模板网格变形为一个拓扑上与模板网格一致、几何上逼近目标模型的头部网格;最后,变形后的模板网格上测点即为目标模型的测点。结果对42名志愿者的三维扫描模型,将本方法所得到的测点与按照测点定义基于人体头面部几何特征所确定的测点进行对比。结果表明,以测点定义所确定的测点为圆心,用本方法所得到的测点,85%以上都落在半径为6mm圆内。结论本研究建立的方法实现了三维头面部测点的自动识别,对特征不明显的测点也能够达到较好的识别,对提高大样本三维头面部人体测量工作的效率有重要意义。     

基于改进Hough变换的直线图形快速提取算法

为能够有效解决实时直线图形提取问题,提出了一种基于多约束Hough变换(HT)的直线提取算法.该算法首先分析了数字图像中直线边缘的三种结构特征,提出采用基元结构表示目标边缘点,并在约束条件下计算基元结构的基元倾角.在此基础上,结合传统的HT的思想对基元结构进行极角约束HT,以获得最终的直线参数.实验结果表明,对合成图像和自然图像,该算法比梯度HT的运算速度分别提高约190倍和22倍.     

Robust and efficient CAD topology generation using adaptive tolerances

A topology generation algorithm, commonly known as geometry repairing/healing, is presented to detect commonly found geometrical and topological issues like cracks, gaps, overlaps, intersections, T‐connections and no/invalid topology in the model, process them and build correct topological information. The present algorithm is based on the iterative vertex pair contraction and expansion operations called stitching and filling, respectively. The algorithm closes small gaps/overlaps via the stitching operation and fills larger gaps by adding faces through the filling operation to process the model accurately. Moreover, the topology generation algorithm can process manifold as well as non‐manifold models, which makes the procedure more general and flexible. Most of the existing techniques use a constant tolerance for topology generation and suffer from the reliability issues and cannot preserve small details of the same size of gaps/overlaps. This algorithm uses an automatic and adaptive tolerance that enhances reliability of the process and preserves small features in the model. In this way, the combination of generality, accuracy, reliability and efficiency of this algorithm seems to be a significant improvement over existing techniques. Copyright © 2007 John Wiley & Sons, Ltd.     

自适应红外图像直方图均衡增强算法

针对红外图像的特点,本文提出了一种自适应红外图像直方图均衡增强算法.该方法根据原始图像的直方图,自适应地构造出一个加权函数对原始图像的直方图进行加权处理,然后采用加权后的直方图对原始图像进行直方图均衡化.算法不需要人为指定阈值,而且克服了传统直方图均衡提升红外图像背景的缺点.实验结果表明,该算法对红外图像具有较好的增强效果,能够有效地抑制图像的背景,突出目标.     

连续体结构的变密度拓扑优化方法研究

为了实现使连续体结构的体积约束和柔顺度最小的拓扑优化及解决采用经典变密度法引起的结构优化结果存在如灰度单元、棋盘格等数值不稳定问题，提出了一种新的拓扑优化方法。首先，采用改进的固体各向同性材料惩罚法作为材料插值方案，建立结构拓扑优化模型；其次，通过引入基于高斯权重函数的敏度过滤法和设计新灰度单元抑制算子来解决数值不稳定问题；最后，借助优化准则法求解优化模型。通过算例分析可知：新策略可以改进拓扑优化方法；新的拓扑优化方法具有收敛速度较快、能更好地获取柔顺度小且拓扑构型好的优化结构和抑制灰度单元产生等优势。研究结果为其他连续体结构的拓扑优化研究提供了新思路。     

Topological Transformation during Normal Grain Growth

This paper investigates topological transformation during normal grain growth by carrying out a computer vertex simulation. Results show that topological correlation agrees with the models proposed by Blanc et al. and Weaire. Topological transformation occurs more often on grains with some topological classes instead of equal probability on each boundary. This can be qualitatively explained by topological correlation.     

Unitary transformation between Cartesian- and polar-pixellated screens

A unitary transformation between Cartesian and polar pixellations of finite two-dimensional images is obtained from the su(2) model for discrete and finite signals. This transformation analyzes the original image into its finite Cartesian "Laguerre-Kravchuk" modes (involving Wigner little-d functions) and synthesizes it back using a polar mode basis with the same set of mode coefficients. The polar basis is derived from the quantum angular momentum theory, and its modes are given by Clebsch-Gordan coefficients.     

A novel level set method for image segmentation by combining local and global information

A novel level set method integrating local and global statistical information is proposed in this paper. In our method, a new signed pressure force (SPF) function is constructed by two parts. One is the global average intensity of the image, which can accelerate the evolution of the curve when the contour far away from the object boundaries. The other is the intensity average of difference image between the averaging convolution image and the original image, which can guide the evolving curve to catch the boundaries of the objects. In addition, an adaptive weighting function is utilized to adjust the ratio between the global and local terms, which can eliminate the inconvenient selection of weighting parameter. By substituting the new SPF function for the edge stopping function of the geodesic active contour model, we obtain a novel adaptive hybrid segmentation model, which is capable of segmenting the images with intensity inhomogeneity. What is more, in our method, the level set function is initialized with a binary function, which reduces the computational cost for the re-initialization step. The experimental results and comparisons with several popular models on synthetic and real images indicate that our method achieves superior performance in segmenting images with noise, low contrast and intensity inhomogeneity.     

Efficient edge-directed deblocking algorithm for KOMPSAT-2 satellite images

In this paper, we present a deblocking algorithm that removes grid and staircase artefacts, which result from the compression of images. The KORea Multi-Purpose SATellite-2 images are efficiently compressed using an algorithm that assigns quantisation coefficients, by rows, according to local region complexity, in such a way that more complicated regions are more compressed. However, this approach is problematic in that it produces blocking artefacts in those relatively less complicated areas within the same row as the complicated regions. Removing these artefacts with a general deblocking algorithm can, undesirably, blur the complicated regions as well as the artefacts; additionally, the general filter is poor at preserving curved edges. Therefore, the algorithm proposed herein presents an adaptive filtering scheme for removing blocking artefacts while preserving image details, including curved edges, using a content analysis. Particularly, edge adaptive filtering has been introduced to remove staircase artefacts around the big image edges. Experimental analyses revealed that the proposed method outperforms several state-of-the-art approaches in terms of subjective or objective image quality, and computational speed.     

A fixed‐grid bidirectional evolutionary structural optimization method and its applications in tunnelling engineering

Topology optimization has exhibited an exceptional capability of improving structural design. However, several typical topology optimization algorithms are finite element (FE) based, where mesh‐dependent zigzag representation of boundaries is barely avoidable in both intermediate and final results. To tackle the problem, this paper proposes a new fixed‐grid‐based bidirectional evolutionary structural optimization method, namely FG BESO. The adoption of an FG FE framework enables a continuous boundary change in the course of topology optimization, which provides a means of dealing with not only the non‐smooth boundary of the final design but also the interpretation of intermediate densities. As a class of important practical application, it is interesting to make use of the new FG BESO method to the reinforcement design for underground tunnels. To accommodate the FG BESO technique to geological engineering applications, a nodal sensitivity is derived for a two‐phase material model comprising the artificial reinforcement and original rock. In this paper, some innovative topological designs of tunnel reinforcements are presented for minimizing the floor and sidewall heaves under different geological loading conditions. Copyright © 2007 John Wiley & Sons, Ltd.     

基于小波的改进加权抛物线插值的图像超分辨率算法

为了尽可能地保持图像的基本信息,提高图像的视觉效果和空间分辨率,提出一种基于小波的改进加权抛物线插值算法,即在传统的加权抛物线算法上增加插值的误差补偿项。利用sobel算子设定插值点的边缘方向,得到初始放大图像。利用小波变换提取高频成份,原始图像幅值增强充当低频部分,再经过小波逆变换得到高分辨率图像。实验结果表明,相对于传统的图像放大算法,该算法考虑到全局相关性,得到更加清晰的边缘信息。