Mesh blending

A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.     

基于结式的曲面拼接与三维造型

针对很多几何造型是带有约束条件的曲面拼接问题,在线性连续同伦的基础上提出了利用非线性同伦连续计算拼接曲面以进行三维造型的方法。首先,根据得到的截面(切片)的位置及其曲线方程确定插值点并得到插值多项式;其次,将此插值多项式作为非线性连续同伦映射函数并分别代入主曲面和辅助曲面的多项式方程得到过渡曲面的方程;然后,仅将插值变元作为变元而主、辅助曲面方程的变元作为参数,利用Sylvester结式消去过渡方程中的变元得到关于主曲面的拼接方程即造型曲面。利用该方法能实现带有控制点的曲面造型以及多曲面约束的几何造型,而且它可以确定造型过程中的中间形状及中间形状的位置,从而更加具有实用性。     

Implementing an extended functional data model using PS-algol

A number of data models have been proposed in the literature to improve the usability of database systems. Most of these proposals have not been implemented. It is felt that a working implementation is a necessary prerequisite for a serious study of these proposals. So far, implementing data models has been a daunting task, requiring a large team. We report here on a simple implementation technique using the persistent algorithmic language. PS-algol, which promises to make the task of implementing data models a manageable one. The feasibility of the approach is demonstrated by implementing an entity-based functional data model (extended function data model) using PS-algol.     

基于Hermite插值的网格拼接和融合

网格模型的拼接和融合是3维形状编辑和造型中的一个重要方面。基于Hermite插值技术,提出一种适用于具有一般边界点空间分布的三角网格模型之间无缝光滑拼接和融合方法。首先查找网格模型待拼接区域的边缘点集,并利用二次B样条曲线插值边缘点集分别得到边缘曲线;然后对边缘曲线进行Hermite插值得到拼接区域连续曲面;最后对拼接曲面分别进行三角网格化和Laplacian光顺平滑处理以实现网格模型的光滑拼接和融合。由于利用B样条曲线插值待拼接模型边界,本文方法适用于具有各种不同边界情形的网格模型拼接和融合,它不仅仅可以处理平面边界曲线情形也可以处理空间边界曲线情形。结合Hermite曲面插值拼接过渡区域,使得产生的拼接网格能光滑地衔接待拼接模型。实验结果表明,本文方法能够有效地实现三角网格模型的光滑拼接、模型修复和模型融合。     

Adaptive mesh refinement using piecewise-linear shape functions based on the blending function method

Use of quadrilateral elements for finite element mesh refinement can lead either to so-called irregular meshes or the necessity of adjustments between finer and coarser parts of the mesh necessary. In the case of irregular meshes, constraints have to be introduced in order to maintain continuity of the displacements. Introduction of finite elements based on blending function interpolation shape functions using piecewise boundary interpolation avoids these problems. This paper introduces an adaptive refinement procedure for these types of elements. The refinement is anh-method. Error estimation is performed using the Zienkiewicz-Zhu method. The refinement is controlled by a switching function representation. The method is applied to the plane stress problem. Numerical examples are given to show the efficiency of the methodology.     

基于Hermite插值的网格拼接和融合-CIDE2013

网格模型的拼接和融合是3维形状编辑和造型中的一个重要方面。基于Hermite插值技术,提出了一种适用于具有一般边界点空间分布的三角网格模型之间无缝光滑拼接和融合方法。首先查找网格模型待拼接区域的边缘点集,并利用二次B样条曲线插值边缘点集分别得到边缘曲线;然后对边缘曲线进行Hermite插值得到拼接区域连续曲面;最后对拼接曲面分别进行三角网格化和Laplacian光顺平滑处理以实现网格模型的光滑拼接和融合。由于利用B样条曲线插值待拼接模型边界,本文方法适用于具有各种不同边界情形的网格模型拼接和融合,它不仅仅可以处理平面边界曲线情形也可以处理空间边界曲线情形。结合Hermite曲面插值拼接过渡区域,使得产生的拼接网格能光滑地衔接待拼接模型。实验结果表明,本文方法能够有效地实现三角网格模型的光滑拼接、模型修复和模型融合。     

基于变分隐式曲面的网格融合

三维物体融合利用三维模型之间的剪贴操作从两个或多个现有的几何模型中光滑融合出新的几何模型.作为一种新的几何造型方法,它正受到越来越多的关注.提出一种基于变分隐式曲面的网格融合新方法.首先利用平面截面切出网格物体的待融合边界,然后通过构造插值待融合网格物体边界的变分隐式曲面并对其进行多边形化,得到待融合网格物体间的过渡曲面,最后通过剪切掉过渡曲面的多余部分及拓扑合并操作以实现过渡网格曲面与原始网格间的光滑融合与现有的直接连接待融合网格物体边界以实现网格融合的算法相比,该方法不仅突破了对待融合物体的拓扑限制,允许多个物体同时进行融合,而且算法计算快速、鲁棒,使用方便,展示出良好的应用前景.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

运用距离测度进行多传感器目标级信息融合

针对多传感器目标级识别问题,提出了一种新的信息融合方法。定义了新的距离测度,通过求解各目标类别与待识别目标的综合距离测度之和最小的优化问题,客观地确定了指标的权重,根据综合距离给出目标识别方法。仿真实验验证了方法的有效性,非常适用于具有多个特征指标的多目标识别领域。     

基于多模型数据融合算法的木材干燥过程建模

提出了一种适合木材干燥过程建模的多模型数据融合算法,通过该方法构建了数据融合模型。分别用BP神经网络和动态递归网络建立了木材干燥基准模型,利用自适应加权算法对两模型输出进行融合,通过实验干燥数据仿真表明:融合后的木材含水率预测值的方差为0.125 3,高于任何一个单独模型的预测精度。     

多传感器图像融合流程建模及仿真

建立图像融合流程并选择最优的融合方法是后续图像融合处理的关键,直接决定了图像融合的效果和应用。分析和研究了多传感器图像融合流程建模方法,建立了一个以图像数据源为入口,以融合模型和融合规则为核心,以融合后图像为输出,以多因素综合判据下的图像融合评价体制为终点的闭环式图像融合流程。最后通过仿真实验,对各自的融合算法和融合规则进行比较,从中选择出容易与主客观评价方法取得一致的,简单实用的,具有与图像数据源和融合目的相适应的融合算法来完成图像融合流程的建模。     

基于VGG-NET的特征融合面部表情识别

为了解决在面部表情特征提取过程中卷积神经网络CNN和局部二值模式LBP只能提取面部表情图像的单一特征,难以提取与面部变化高度相关的精确特征的问题,提出了一种基于深度学习的特征融合的表情识别方法。该方法将LBP特征和CNN卷积层提取的特征通过加权的方式结合在改进的VGG-16网络连接层中,最后将融合特征送入Softmax分类器获取各类特征的概率,完成基本的6种表情分类。实验结果表明,所提方法在CK+和JAFFE数据集上的平均识别准确率分别达到了97.5%和97.62%,利用融合特征得到的识别结果明显优于利用单一特征识别的效果。与其他方法相比较,该方法能有效提高表情识别准确率,对光照变化更加鲁棒。     

基于知识融合技术的产品设计知识模型研究*

为解决机械产品设计中知识融合的问题,建立了一种基于本体的产品设计知识模型。基于此,提出了一种基于模糊证据理论的融合推理方法,这种方法将模糊集引入证据识别空间中,利用证据合成与决策规则得出知识融合的结果,这种方法弥补了本体建模时的不足并解决了不确定性知识的推理。最后,将该知识模型应用于制造行业产品设计中,帮助制造企业缩短产品设计周期和提高开发质量,同时也有利于产品设计知识的共享和重用。     

基于支持向量机分数等级融合的虹膜识别方法

提出了一种使用支持向量机（Support Vector Machine,SVM）的分数等级融合的虹膜识别方法。通过对虹膜纹理采用小波包分解,选择最高能量区域和次高能量区域提取特征向量,与注册入库的虹膜特征向量计算出海明距离。最后融合两个海明距离输入SVM进行识别。该方法减少输入支持向量机的维数。实验结果表明,该法提高了识别率,能够有效地应用到身份鉴别系统中。     

基于特征融合的多模态身份识别方法研究

研究了多模态身份识别问题,结合人脸和掌纹两种不同生理特征,提出了基于特征融合的多模态身份识别方法。对人脸和掌纹图像分别进行Gabor小波、二维主元变换(2DPCA)提取图像特征,根据新的权重算法,结合两种模态的特征,利用最邻近分类器进行分类识别。在AMP、ORL人脸库和Poly-U掌纹图像库中的实验结果表明,两种模态的融合能更多地给出决策分析所需的特征信息相比传统的单一模态的人脸或掌纹识别具有较高的识别率,更具安全性和准确性。     

基于小波Contourlet变换和区域能量融合规则的图像融合算法*

利用小波-Contourlet变换的多尺度和多方向性特征以及基于区域能量的融合规则在选取融合系数上的优势,提出了一种基于小波-Contourlet变换和区域能量融合规则的图像融合算法,并引入Cycle Spinning来消除变换缺乏平移不变性而引起的图像失真。实验结果表明,该算法明显降低了融合图像的RMSE值,减少了运算时间,达到了良好的视觉效果。     

基于SWT区域对比度像素提取的多聚焦图像融合

在多聚焦图像融合算法中,针对多分辨率系数融合法无法提取源图像清晰像素点和分块法存在的块效应的现象,从多聚焦图像清晰像素点和人眼视觉对比度的特征出发,利用平稳小波变换(SWT)的非下采样性和平移不变性,定义基于SWT的图像像素点区域对比度作为提取像素点的依据,在研究区域对比度邻域大小对像素点提取影响的基础上,设定适合的阈值建立提取模板,对多聚焦源图像中清楚区域的像素点进行提取,并对小部分未能提取的像素位置采用基于局部能量策略进行融合。仿真实验结果表明,新算法既有效地提取源图像的清晰像素点,又改善了块效应现象,融合效果有了很大提升。     

利用多颜色信息融合的自适应肤色建模

客观世界中存在不同的光照、肤色和人种等因素,通常情况下难以建立一个通用的肤色模型进行各种裸露皮肤的检测。研究了一种自适应肤色建模方法,即利用AdaBoost算法检测人脸,通过这些人脸区域进行肤色建模。为了取得较好的肤色建模效果,适当缩小了由AdaBoost算法检测到的人脸区域;利用多颜色空间信息融合技术,即通过选取多个颜色空间的若干颜色分量,计算待检测图像中这些分量的各自SPM（肤色概率图）,经过“与”运算融合获得最终检测的肤色区域。该算法不需要考虑光照、肤色和人种等因素,是一种自适应的建模过程。实验表明,该算法可以有效解决绝大多数情况下的彩色图像肤色检测问题。     

基于小波变换的Cycle Spinning图像融合的扩展研究

Cycle Spinning是在图像去噪时使用的一种消除伪吉布斯（Pseudo-Gibbs）现象的方法,目前的文献中较少有使用该方法进行图像融合的研究。尝试结合小波变换和Cycle Spinning循环平移的思想,提出了一种基于小波变换的Cycle Spinning图像融合方法,并在此基础上对平移进行了扩展研究。仿真实验证明该方法与传统DWT方法相比,能较好地保留图像的细节信息,获得更高的PSNR值。     

Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.