A methodology for shape matching of non-rigid structures based on integrated graphical information

Shape matching is a long-studied problem and lies at the core of many applications in statistical shape analysis, virtual reality and human–computer interaction. This paper presents an automatic dense correspondence method to match the mesh vertices of two 3D shapes under near-isometric and non-rigid deformations. The goal is achieved by combining three types of graphic structure information. The method includes three major steps: first, we describe the vertices based on three types of graphical information, Euclidean structure information, Riemannian structure information, and conformal structure information; second, the match between two shapes is formulated as an optimization problem and a novel objective function is proposed; third, we resolve the optimal solution by using the projected descent optimization procedure to solve the objective function. The method is tested on various shape pairs with different poses, surface details, and topological noises. We demonstrate the performance of our approach through an extensive quantitative and qualitative evaluation on several challenging 3D shape matching datasets where we achieve superior performance to existing methods.     

基于PVDF传感器和工作曲率变形实现裂纹梁裂纹检测

提出通过压电高分子薄膜PVDF(Polyvinylidene Fluoride)作为传感器直接测量结构的工作曲率变形(Curvature Operating Shape)实现结构裂纹检测.以悬臂裂纹梁为例,首先探讨基于PVDF传感器测量工作曲率变形,并用于结构损伤检测的可行性,然后介绍工作曲率变形用于损伤检测的理论基础,并以PVDF压电薄膜作为传感器,提出只通过损伤后结构的频率响应函数,直接得到工作曲率变形和损伤指标.最后通过对一条裂纹和两条裂纹悬臂梁的损伤检测实验,证明该方法在梁结构损伤识别和健康监测中的有效性,并且识别过程不需要健康结构的振动信息.     

A new method for linear feature and junction enhancement in 2D images based on morphological operation,oriented anisotropic Gaussian function and Hessian information

Feature enhancement is an important preprocessing step in many image processing tasks. It is the process of adjusting image intensities so that the enhanced results are more suitable for analysis. Good enhancement results for linear structures such as vessels or neurites can be used as inputs for segmentation and other operations. In this paper, a novel linear feature enhancement filter – an adaptive multi-scale morpho-Gaussian filter – which can enhance and smooth linear features is proposed based on morphological operation, anisotropic Gaussian function and Hessian information. This filter can enhance and smooth along the local orientation of the linear structures and the Hessian measurement is used to further enhance the linear features. We utilize the Hessian matrix to calculate the orientation information for our directional morphological operation and the oriented anisotropic Gaussian smoothing. We also propose a novel method for junction enhancement, which can solve the problem of junction suppression. We decompose the junctions and enhance along each linear structure within a junction region. We present the test results of our algorithm on images of different types and compare our method with three existing methods. The experimental results show that the proposed approach can achieve better results.     

Hyperspectral image classification based on bilateral filter with linear spatial correlation information

Support Vector Machine (SVM) with the margin theory is widely used for the hyperspectral classification. However, the margin model is a single interval and does not represent the complete distribution of hyperspectral image data sets. In addition, the spatial texture information obtained by filtering in recent years has become a hot research topic for improving classification of hyperspectral images, but the spatial correlation information is often lost in the spatial texture information extraction. To solve this problem, this paper proposed an algorithm with large margin distribution machine (LDM) that combined the spatial information obtained by the bilateral filter and linear spatial correlation information (BFLSCI-LDM). First, spatial features were extracted by bilateral filter from hyperspectral image whose dimensionality was reduced by principal component analysis. Next, the linear spatial correlation information was constructed for hyperspectral images. Finally, the spatial information and original spectral information were combined for LDM. The experimental results of actual hyperspectral images indicated that the proposed BFLSCI-LDM method was superior to other classification methods, including the original SVM with the raw spectral features, the dimensionality reduction features, and spatial-spectral information, the method of edge-preserving filter and recursive filter, and the LDM-based method.     

结合先验形状信息和序贯学习的心血管内超声外弹力膜检测

目的 针对心血管内超声(IVUS)图像中钙化斑块、声影等干扰因素影响外弹力膜(EEM)轮廓检测准确性的问题,提出结合先验形状信息和序贯学习分类的心血管内超声外弹力膜检测的改进算法。方法 首先用多类多尺度序贯学习(M²SSL)将IVUS图像分割七大不同组织;然后在分类结果的基础上,结合血管先验形状信息筛选出外弹力膜轮廓的关键点;最后,结合IVUS图像的梯度和相位信息,采用Snake模型,获得最终的EEM轮廓。结果 临床采集22组IVUS序列,挑选出具有代表性的153帧图像做实验。统计数据显示:本文算法检测结果的平均Jacc指标为88.5%,满足临床诊断要求,性能优于国内近年来较好的算法。结论 本文的EEM自动检测算法简单有效,相比国内已有算法,提高了对钙化、纤维斑块以及声影区域的识别能力,对含钙化斑块、纤维斑块或血管中心偏移的高频IVUS图像具有较高的适用性。     

Image feature detection algorithm based on the spread of Hessian source

Image feature detection can be obtained from many methods including the feature point detection. This paper adopts the image feature point detection method based on second-order characteristics of point and the image feature detection algorithm based on the Hessian matrix to detect more feature points. By combining the gray-scale-based image-matching technology with the feature-based image feature detection technology, we propose a Hessian algorithm to obtain more matching points, which can search for matching more quickly. The proposed algorithm overcomes the traditional matching methods that have Ergodic properties of the search strategy. Experiments demonstrate the speed and accuracy of the proposed algorithm, and we use the correct detected feature points to realize image registration, image fusion and image stitching.     

基于SCADA参量关联互信息自编码的风电机组故障检测方法

针对风力发电机组数据采集与监视控制系统(supervisory control and data acquisition,SCADA)监测参量间的耦合关联性,提出基于多参数耦合关联互信息编码的风电机组故障检测方法.该方法构建了SCADA数据的耦合关联矩阵,采用互信息变分自编码器对关联矩阵进行编码重构;将SCADA参量关联矩阵的重构误差作为机组健康评估指标,结合指数加权移动平均模型的迭代更新,对机组实时故障阈值进行自适应设置.两个风场的风电机组SCADA数据分析结果表明,所提方法充分利用了SCADA数据的耦合关联结构信息,能有效提高风电机组故障检测的准确性及对环境工况的鲁棒性.     

基于直线模型的车道线实时检测方法

为提高车道线检测的精度和实时性,提出了一种基于直线模型的实时车道线检测方法.采用改进的Sobel算子进行边缘检测,利用自适应双阈值的方法进行图像的二值化.对基于Hough变换的车道线检测方法进行了改进,令识别视觉效果大大提升.并利用Kalman滤波器来动态确定感兴趣小窗口的大小和位置,实现后续帧道路图像的跟踪.实验结果表明,该方法准确性高,具有较好的实时性和鲁棒性.     

基于局部线性相关的信号去噪方法

针对基于整体线性逆问题的信号去噪方法会导致信号去噪不充分以及严重丢失细节的问题,提出一种建立在局部线性相关基础上的信号去噪方法。该方法以带噪声信号与原始信号局部存在线性相关性为基础,首先利用信号局部具有相同的尺度系数与偏移量,构造信号匹配模型;然后以原始信号的1-范数构造正则项;最后利用快速收缩算法求解去噪模型,使收敛速度达到二阶收敛。实验结果表明,本文方法稳定性强、鲁棒性好,在去噪的同时较好的恢复了信号的高频分量。     

形状感知的绝缘子识别与缺陷诊断

目的 在无人机检测输电线路缺陷的研究中,为提高识别绝缘子的正确率,克服基于颜色来识别绝缘子方法的不足,依据绝缘子串的形状结构特征,研究了一种自底向上感知聚类平行线段的方法。方法 首先将在巡检图像上提取到所有方向的分段划分为6组方向线段,在每一组方向线段中,将线段长度、方向及中心点排列方向一致的线段聚类为平行线组,将平行线组合并,并整理其外接形状,结合输电线路知识模型,可靠识别绝缘子区域。为诊断玻璃绝缘子的掉片缺陷,依据计算出绝缘子的排列方向及片之间距离进行自适应分块,计算每一块的惯性矩均值特征量与惯性矩方差值特征量,依据分块之间特征量相似度来诊断是否存在掉片缺陷。结果 相比基于HSI颜色识别绝缘子的方法,识别绝缘子内部的多平行线段的结构,表现得更稳定,更适用于输电线路巡检。结论 通过无人机巡检采集的输电线路图像,实验结果验证这种方法在复杂背景的条件下能有效识别各种类型绝缘子并能检测绝缘子的掉片缺陷。     

综合颜色和形状的圆形交通标志检测方法

快速、可靠的交通标志检测是对其进行准确识别的前提,以颜色分割为基础,提出了一种基于曲线拟合的圆形交通标志检测算法。首先利用交通标志的颜色特征预分割出潜在的交通标志区域,然后针对圆形交通标志轮廓具有圆形这一关键特征,通过边缘检测并采用非线性最小二乘技术准确的确定出图像中的圆形交通标志区域。实验结果表明了算法的有效性。     

Optimum shape design of truss structures based on reliability

A procedure is presented for the shape optimization of truss structures based on the reliability concept. Nodal coordinates are taken as the shape design variables together with the sizing design variables such as the cross-sectional areas of the members. These variables are determined to minimize the structural volume under the constraint on the structural failure probability.     

3D shape recognition based on multi-modal information fusion

The classification and retrieval of 3D models have been widely used in the field of multimedia and computer vision. With the rapid development of computer graphics, different algorithms corresponding to different representations of 3D models have achieved the best performance. The advances in deep learning also encourage various deep models for 3D feature representation. For multi-view, point cloud, and PANORAMA-view, different models have shown significant performance on 3D shape classification. However, There’s not a way to consider utilizing the fusion information of multi-modal for 3D shape classification. In our opinion, We propose a novel multi-modal information fusion method for 3D shape classification, which can fully utilize the advantage of different modal to predict the label of class. More specifically, the proposed can effectively fuse more modal information. it is easy to utilize in other similar applications. We have evaluated our framework on the popular dataset ModelNet40 for the classification task on 3D shape. Series experimental results and comparisons with state-of-the-art methods demonstrate the validity of our approach.

     

基于灰度相关性的裂缝检测算法

从裂缝的灰度特征入手, 提出一种基于灰度值相似度的裂缝提取方法。针对混凝土路面孔洞、污损等因素干扰, 主要分三步实现裂缝提取：a)对所采集的路面图像进行特征分析, 建立灰度值相似度函数, 根据相似度对比提取疑似裂缝信息; b)利用连通域度量去除虚假裂缝, 将问题转换为图论问题; c）利用谱聚类算法提取真实裂缝。通过大量混凝土路面图片测试表明, 该方法能实现路面真实裂缝特征提取, 具有较强实用性。     

基于权限相关性的Android恶意软件检测

针对Android平台恶意软件检测需求和Android权限特征冗余的问题,提出一套从权限相关性角度快速检测恶意软件的方案。采用卡方检验计算各权限属性对于分类结果的影响大小,去除冗余权限特征,再对权限属性聚类,提取代表性权限特征,进一步减少冗余。最后利用基于不同权限特征权重的改进朴素贝叶斯算法进行软件分类。在收集的2000个软件样本上进行了实验,恶意软件漏检率为10.33%,总体预测准确率达到88.98%。实验结果表明,该方案利用少量权限特征,能够初步检测Android应用软件是否有恶意倾向,为深入判断分析提供参考依据。     

基于通道间相关性的图像重着色检测

图像重着色是一种新兴的图像编辑技术,通过篡改像素值达到改变图像颜色风格的目的。随着社交网络和图像编辑技术的快速发展,重着色图像已经严重阻碍了信息传达的真实性。然而,专门为重着色而设计的工作少之又少,现有的重着色检测方法在传统重着色场景下仍有很大提升空间,在应对手工重着色图像时效果不佳。为此,提出了一种基于通道间相关性的重着色图像检测方法,该方法适用于重着色任务中的传统重着色和手工重着色场景。基于相机成像和重着色图像生成方式之间存在显著差异这一现象,提出重着色操作或许会破坏自然图像的通道间相关性这一假设。通过数值分析说明,通道间相关性差异可作为区分重着色图像和自然图像的重要鉴别度量。基于上述先验知识,所提方法通过提取差分图像的一阶微分残差的通道共生矩阵,获得图像的通道间相关性特征集。此外,根据实际情况,假设了3种检测场景,包括训练-测试数据之间匹配、不匹配以及手工重着色场景。实验结果表明,所提方法能够准确识别重着色图像,在假设的3种场景下均优于现有方法,取得了较高的检测精度。除此之外,所提方法对训练数据量的依赖性较小,在训练数据有限的情况下,能实现相当精确的预测结果。     

Objective reduction based on nonlinear correlation information entropy

It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multi-objective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). Objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs.     

基于脸部信息和支持向量机的人脸检测

人脸检测是一个复杂而又非常有意义的模式识别问题。针对目前人脸检测算法于速度和精度不能兼优的问题,提出了一种基于脸部信息及支持向量机的人脸检测方法。算法首先利用肤色模型进行人脸粗检,然后根据人脸几何特征进行筛选,最后通过奇异值分解输入支持向量机分类。实验结果表明,该方法是十分有效的。     

A parallel algorithm for detection of linear structures in satellite images

To enhance linear structures in a gray level image, local operations with an additive score are normally used. Here a multiplicative score is used instead which gives better results than the additive one. The problem of segmenting the image of the multiplicative score is then dealt with where the threshold value can be automatically selected. The experimental results on some satellite images are reported.