High-resolution Remote Sensing Image Semi-global Matching Method Considering Geometric Constraints of Connection Points and Image Texture Information

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture.123123The method includes 4 parts:①Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;②Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;③Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;④Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.

Semi-Global Inference in Phenotype-Protein Network

Discovering genetic basis of diseases is an important goal and a challenging problem in bioinformatics research. Inspired by network-based global inference approach, Semi-global inference method is proposed to capture the complex associations between phenotypes and genes. The proposed method integrates phenotype similarities and protein-protein interactions, and it establishes the profile vectors of phenotypes and proteins. Then the relevance between each candidate gene and the target phenotype is evaluated. Candidate genes are then ranked according to relevance mark and genes that are potentially associated with target disease are identified based on this ranking. The model selects nodes in integrated phenotype-protein network for inference, by exploiting Phenotype Similarity Threshold (PST), which throws lights on selection of similar phenotypes for gene prediction problem. Different vector relevance metrics for computing the relevance marks of candidate genes are discussed. The performance of the model is evaluated on Online Mendelian Inheritance in Man (OMIM) data sets and experimental evaluation shows high performance of proposed Semi-global method outperforms existing global inference methods.

High-resolution Remote Sensing Image Semi-global Matching Method Considering Geometric Constraints of Connection Points and Image Texture Information

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on the large amount of high-resolution remote sensing image data and the characteristics of clear image texture.The method includes 4 parts:(1)Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;(2)Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;(3)Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;(4)Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.

Semi-Global Practical Stability of Random Systems and Its Application

In this paper,a new stochastic analysis tool on semi-global stability is constructed,for nonlinear systems disturbed by stochastic processes with strongly bounded in probability.The definition of semi-global noise to state practical stability in probability and its Lyapunov criterion for random systems are presented.As a major application of stability,the semi-global practical tracking of random nonlinear systems based on dynamic surface control technique is considered.The trajectory tracking of manipulator robot driven by direct current motors is carried out in simulation to illustrate the effectiveness and feasibility of the control scheme.

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.

SEMI-GLOBAL C^1 SOLUTION TO THE MIXED INITIAL-BOUNDARY VALUE PROBLEM FOR QUASILINEAR HYPERBOLIC SYSTEMS

By means of an equivalent invariant form of boundary conditions, the authors get the exis- tence and uniqueness of semi-global C1 solution to the mixed initial-boundary value problem for quasilinear hyperbolic systems with general nonlinear boundary conditions.

Semi-Global Stabilization via Homogeneous Output Feedback for a Class of Nonlinear Time-Delay Systems

The semi-global stabilization problem for a class of nonlinear systems with state time-delay is addressed in this paper.By using Lyapunov-Krasovskii functional method and homogeneous domination approach,a homogeneous observer and an output feedback controller with a scaling gain are designed.Then the scaling gain is adjusted such that the closed-loop system is semi-global asymptotically stable.A numerical example is presented to illustrate the effectiveness of the obtained results in this paper.

基于Pynq的半全局立体匹配算法实现

立体匹配算法作为双目立体视觉技术的核心算法,具有运算复杂度高、资源占用率高、实时性差的特点。而现有的基于FPGA实现方法多采用高性能FPGA芯片,利用其板卡丰富的资源进行纯verilog语言开发。这样做虽然实时性效果不错,但存在开发难度较大、周期较长、成本较高的弊端。论文针对该问题,提出了一种基于相对低廉的Pynq-z2开发板在PL+PS端协同开发的SGM立体匹配算法,将运算量大的部分在FPGA端进行硬件加速,部分代价优化算法在ARM端利用Python语言实现。实验表明,该算法可有效降低资源占用率,基本满足在相对低廉的嵌入式设备中实现的要求。

三通道多姿态面部正面化方法

针对现有面部正面化网络在复杂环境下难以保留面部显著性特征的问题,提出一种三通道(局部、半全局和全局)面部正面化方法。在TP-GAN算法的原有框架基础上设计半全局网络,融合全局网络和局部网络之间的依赖关系,使生成的正面化图像的分布与真实面部图像更接近;在半全局网络中设计多时空深度注意力模块,促进网络学习到更多面部显著性特征;将所提方法应用于CAS-PEAL-R1数据集和自建数据集,采用Rank-1指标进行评估。实验结果表明,所提方法在所有角度下的Rank-1平均准确率为99.40%,验证添加了多时空深度注意力模块的半全局网络可以有效保留面部显著特征,提高面部匹配准确率。

基于半监督学习代理辅助的混合进化算法

针对目标函数评价昂贵的优化问题,在计算资源有限的情况下很难获得足够数据训练一个准确的全局代理模型,然而,不准确的全局代理模型其潜在优势是可以平滑局部极值点,从而可以引导算法加速找到最优解。另一方面,局部模型虽然不能辅助算法跳出局部最优,但是其相对于全局模型在局部区域具有较好的拟合效果。本文利用这两类模型的优点,针对计算昂贵问题提出了基于半监督学习代理模型的混合进化算法(SSL-SAHA)。在现有算法的基础上,对局部搜索部分进行了改进。利用在全局搜索过程中建立的集成模型选择一些未真实计算的个体,一起用于训练局部模型,从而提高局部RBF模型的估值准确度。实验结果表明,此算法可以有效求解计算昂贵问题。

基于SIFT算法的电动车充电孔双目视觉识别技术

基于双目视觉理论提出一种适用于不同光强、背景和任意形状充电插孔的高精度充电孔识别与定位方法。采用SIFT特征提取算法生成高斯差分金字塔进行尺度空间构建,应用机器学习最近邻搜索特征匹配算法获取匹配点映射集合,并配合半全局匹配方法进行视差计算得到高精度充电孔匹配和定位结果。通过试验验证了所提算法的有效性,得到了较为理想的匹配与测距结果。

基于双目视觉的堆叠零件识别和定位研究

针对堆叠零件识别与定位精度不高与效率低下等问题,设计了一种基于双目视觉的堆叠零件识别与定位方法。利用双目视觉中的立体匹配,结合前景分割、孔洞填充、随机抽样一致性(RANSAC)迭代法剔除误匹配点,对半全局块匹配(SGBM)算法进行改进,获取更优的视差效果图。最后,对圆形线圈骨架堆叠零件进行了测量精度和定位精度实验。实验结果表明:改进算法能够很好地解决弱纹理和遮挡区的匹配问题,系统测量精度实际偏差控制在1 mm以下,在实际应用方面具有一定参考价值。

一个一维二阶拟线性双曲组的精确边界能控性

In this paper,we propose a second-order quasilinear hyperbolic system.By means of the theory on semi-global C^(1)solution to first-order quasilinear hyperbolic systems,we establish the existence and uniqueness of semi-global C^(2)solution to this second-order quasilinear hyperbolic system.After then,the general constructive framework is utilized to obtain the local exact boundary controllability for this second-order system.

基于半全局匹配的去遮挡方法研究

针对合成孔径集成成像系统中前景遮挡影响目标物的采集和识别的问题,提出了一种基于半全局立体匹配的去遮挡方法。首先搭建相机阵列采集目标物的前景遮挡图片,接着采用数字重聚焦技术和能量梯度函数设置分类阈值,将遮挡物和目标物进行分类并去除前景遮挡,然后利用元素图像修复技术对遮挡去除区域进行修复,最后对修复后的元素图像进行目标物重建。实验结果表明,本文方法重建出的结果图相比于传统的合成孔径成像方法在峰值信噪比(PSNR)上提高了13.271 4dB,相比块匹配方法在峰值信噪比(PSNR)上提高了5.350 3 dB。

燃料电池模型多尺度参数双代价函数的全局灵敏度分析

质子交换膜燃料电池半经验模型存在较多未知经验参数,且其尺度和取值范围均相差较大,在全局灵敏度分析中局部高灵敏度信息易丢失,从而导致结果存在偏差。为此,构造了互为倒数的双代价函数。在原代价函数基础上计算全域内经验参数与响应误差的相关性,利用Sobol方法均匀采样进行首次全局灵敏度分析,筛选整个取值范围内高灵敏度(即影响收敛速度)参数;进而利用倒数代价函数放大局部区域的误差相关性,对于剩余全域不敏感参数,进行倒数代价函数的再次全局灵敏度分析,筛选出局部取值范围内高灵敏度(即影响收敛精度)参数。从而提高了多尺度多局域高灵敏参数识别能力。分析结果证明了所得高灵敏度参数辨识后模型的响应误差与全参数辨识的结果一致,可节省约60%的计算成本。该方法的适用性和准确性在电堆实验中均得到了有效验证。

高分七号卫星立体影像精细三维重建

为了实现高分七号卫星亚米级影像的高精度精细三维重建,针对高分七号卫星前后视线阵相机的成像特点,围绕影像的定向、影像畸变消除、密集匹配方法优化等方面,提出一套完整的精细三维重建方法和流程。利用影像间连接点几何约束关系对有理函数模型进行了二次定向消除系统误差;其次采用一种基于物方投影面的水平纠正方法对原始影像进行纠正,消除了立体像对之间大的倾斜误差;在密集匹配阶段,将全球公开数字高程模型(Digital Elevation Model,DEM)数据作为视差约束,同时引入顾及影像灰度和特征信息的AD-Census作为匹配测度,有效削弱重复纹理引起的匹配错误问题,提升了数字表面模型(Digital Surface Model,DSM)的质量。利用覆盖我国宁夏和新疆某地的两组高分七号的立体影像进行了试验,结果表明本文所提出方法可将相对误差精度由平差前的0.847 pixel和0.725 pixel分别提升到平差后的0.652 pixel和0.593 pixel,相对误差精度最大可提高23.02%,基于物方投影面的水平纠正方法能够显著消除大倾角差异带来的几何畸变,对高分七号卫星影像取得了较好质量的DSM产品,尤其是对于小尺度密集建筑物区域的重复纹理取得了较好的效果。

无横撑六立柱半潜平台总强度研究

对无横撑六立柱半潜平台的结构强度进行研究。总结该平台的研究现状,阐述其结构总强度分析流程,介绍该类型平台总强度分析的计算工况,确定目标无横撑半潜平台的设计波参数,探讨结构重点关注区域。通过与传统四立柱平台的结构总强度计算结果相对比,分析六立柱半潜平台与四立柱半潜平台在总强度方面的差异。通过对有无横撑2种型式半潜平台结构的应力情况进行对比,分析横撑结构在该类型平台设计中应用的必要性,为无横撑半潜平台的结构设计提供参考。

节点半刚性特性对空间结构整体稳定性的影响分析

空间结构节点连接构造形式复杂且多样,节点刚度与其构造方式密切相关。基于刚接或铰接的传统边界假定不能完全代表部分节点的特性。空间结构的节点刚度对结构整体稳定性影响显著。以刚接或铰接为连接假定的传统结构计算方法可能会给空间结构的整体稳定分析带来不可忽略的误差。以具体工程为案例,阐述了利用节点有限元分析法和非线性结构计算手段对半刚性空间结构进行整体稳定性分析的过程。通过比较刚接和半刚性模型的计算结果,论证了空间结构设计过程中准确评估节点半刚性特性的必要性。

双变量多单元半全局极值搜索算法研究

传统的双变量多单元半全局极值搜索算法虽然可以放宽目标函数的限制条件,但是它只适用于静态系统的分析。针对动态系统,研究了双变量多单元半全局极值搜索算法,提出了1种利用单元的输入和动态误差项构造李亚普诺夫函数的方法,并且证明了算法稳定性。最后,针对典型通用多极值测试函数进行了数字仿真研究。