Image and model sequences matching for on-site assembly stage identification

In an assembly task, stage identification determines the subsequent parts to be assembled and therefore, it affects the quality and efficiency of the entire assembly operation in computer-aided manufacturing. However, due to the similarity between the assemblies in two adjacent stages, it is difficult to distinguish the assembly accurately based on the current visual recognition method. Considering the time and progress logic of assembly procedures, a stage identification method based on the on-site assembly sequence and stage model sequence is proposed. The on-site assembly sequence is generated with the assembly progress to record the history information in the time order and the stage model sequence is preprocessed in the database in the progress order. On the one hand, these two sequences provide the constraints to avoid the inefficient and indistinguishable assembly images to be analyzed. On the other hand, the two sequences contain time and assembly progress logic, which can be used to solve the stage identification issue using an optimal mapping model, so as to improve the identification efficiency. This paper introduces the methodology from three aspects, namely, offline stage model sequence generation, online assembly sequence matching and assembly stage identification. The experiments proved the proposed method can effectively improve the accuracy of real-time assembly stage identification compared with the original object recognition method.     

一种基于角点特征的图像自动配准方法

图像配准是图像处理和计算机视觉中的重要环节。提出了一种基于角点特征的图像自动配准方法来处理具有相似变换的图像配准问题。角点特征由改进的Harris算子提取,然后将提取的角点组成虚拟三角形,利用在相似变换下参考图像和待配准图像中对应的虚拟三角形相似的原理,找到最相似的两个虚拟三角形,以它们对应的顶点作为控制点,求出变换模型参数,从而配准两幅图像。该方法只要求两幅图像中提取的角点特征包含3个以上的对应角点,就能配准两幅图像。它的另一个优点是理论上对两幅图像之间发生的平移、旋转和尺度变化没有限制。实验结果表明:这种图像自动配准算法是正确和有效的。     

Corner validation based on extracted corner properties

We developed a method to validate and filter a large set of previously obtained corner points. We derived the necessary relationships between image derivatives and estimates of corner angle, orientation and contrast. Commonly used cornerness measures of the auto-correlation matrix estimates of image derivatives are expressed in terms of these estimated corner properties. A candidate corner is validated if the cornerness score directly obtained from the image is sufficiently close to the cornerness score for an ideal corner with the estimated orientation, angle and contrast. We tested this algorithm on both real and synthetic images and observed that this procedure significantly improves the corner detection rates based on human evaluations. We tested the accuracy of our corner property estimates under various noise conditions. Extracted corner properties can also be used for tasks like feature point matching, object recognition and pose estimation.     

Digital twin-based assembly data management and process traceability for complex products

Complex products such as satellites, missiles, and aircraft typically have demanding requirements for dynamic data management and process traceability. The assembly process for these complex products involves high complexity, strong dynamics, many uncertainties, and frequent rework and repair, especially in the model development stage. Achieving assembly data management and process traceability for complex products has always been a challenge. A recently proposed solution involves one-to-one mapping of the corresponding physical entity, also known as the digital twin method. This paper proposes a digital twin-based assembly data management and process traceability approach for complex products. First, the dynamic evolutionary process of complex product assembly data was analyzed from three dimensions: granularity, period and version. Then, a framework of digital twin-based assembly data management and process traceability for complex products was constructed. Some core techniques are: 1) workflow-based product assembly data organization and version management; 2) synchronous modeling of the product assembly process based on digital twin; and 3) hierarchical management and traceability of product assembly data based on digital twin. On this basis, an algorithm flowchart for generating a product assembly data package was created, which includes product assembly data management, assembly process traceability, and generation of a product assembly data package. Furthermore, the Digital Twin-based Assembly Process Management and Control System (DT-APMCS) was designed to verify the efficiency of the proposed approach. Some aerospace-related assembly enterprises are currently using DT-APMCS and achieving satisfactory results. Finally, a summary of our work is given, and the future research work is also discussed.     

基于图像轮廓曲线的拐点检测算法

拐点是图像很重要的特征,包含图像的主要结构信息，拐点检测对图像配准、目标识别和三维重建等任务的处理都有着非常重要的作用。从研究轮廓上点的曲率出发，针对传统基于轮廓的拐点检测算法对噪声和局部变化敏感而造成检测结果不稳定的问题，提出一种间接反映轮廓曲率特性的方法，记为APTD(Accumulation of Point to Tangent Distance)。使用高斯函数对轮廓曲线进行平滑处理，轮廓上点的曲率越大则该点附近的点到该点处切线的距离也越大，根据这一思想，将轮廓上点附近支撑域内的点到该点所对应切线相对距离的累加和作为拐点的判别函数，从而实现轮廓拐点的检测。经由数学推导表明所提出的算法具有合理性和可行性，对比实验分析表明该算法精度高、运算量小、定位准确。     

一种混杂的图像配准方法

提出一种基于特征和亮度的混杂图像配准方法．首先对源图像进行小波分解，在小波金字塔最顶层的低频近似子图像上进行角点检测，通过仿射变换模型得到初始配准参数，然后将这一参数逐层进行迭代精炼，直至达到金字塔最底层，得到最终精确的配准参数．将这些不同配准方法结合起来，可以克服单独使用任一方法存在的不足,理论分析和实验结果表明，这种混杂图像配准算法精度较高．     

Scheduling of multi-component products in a two-stage flexible flow shop

In this research, the problem of scheduling and sequencing of two-stage assembly-type flexible flow shop with dedicated assembly lines, which produce different products according to requested demand during the planning horizon with the aim of minimizing maximum completion time of products is investigated. The first stage consists of several parallel machines in site I with different speeds in processing components and one machine in site II, and the second stage consists of two dedicated assembly lines. Each product requires several kinds of components with different sizes. Each component has its own structure which leading to difference processing times to assemble. Products composed of only single-process components are assigned to the first assembly line and products composed of at least a two-process component are assigned to the second assembly line. Components are placed on the related dedicated assembly line in the second phase after being completed on the assigned machines in the first phase and final products will be produced by assembling the components. The main contribution of our work is development of a new mathematical model in flexible flow shop scheduling problem and presentation of a new methodology for solving the proposed model. Flexible flow shop problems being an NP-hard problem, therefore we proposed a hybrid meta-heuristic method as a combination of simulated annealing (SA) and imperialist competitive algorithms (ICA). We implement our obtained algorithm and the ones obtained by the LINGO9 software package. Various parameters and operators of the proposed Meta-heuristic algorithm are discussed and calibrated by means of Taguchi statistical technique.     

基于凹点检测的卵母细胞极体识别方法

为解决卵母细胞极体不明显时无法有效识别的问题,提出一种基于凹点检测的极体识别方法.结合Otsu算法和形态学操作提取细胞轮廓;通过角点检测和圆形掩膜方法搜索轮廓上的深凹点,设计自适应确定掩膜半径方法和凹凸特征参数判别深浅凹点,筛选出卵母细胞与极体粘连形成的深凹点,确定识别结果.实验结果表明,该方法在极体不明显时识别准确率...     

结合模型限制和灰度信息的图像配准算法

由于噪声和成像条件的不同,准确得到两幅图像中提取的点特征之间的对应性是基于点特征的图像配准方法的一个难点.为此,文中提出了一种新的基于点特征的图像配准算法.该方法结合图像之间的拓扑关系和点特征之间的相似性,并利用变换模型限制求解点特征之间的对应性,然后根据对应的点特征求解变换模型参数,配准两幅图像.实验结果表明,所提出的方法对传感器相近的图像之间的配准是正确有效的.     

模糊图像中的飞机识别方法

针对模糊图像中飞机识别问题,提出一种基于角点和核聚类的飞机识别方法。该方法提取图像的角点特征,对角点进行核聚类,根据聚类结果识别飞机的角点,完成飞机的定位识别。在核聚类算法中,引入有效性函数,能自适应的确定聚类数目,解决了现有核聚类算法需要事先确定聚类数的弱点。实验表明,角点特征具有位移、旋转、尺度不变性,自适应核聚类算法能准确识别模糊图像中的飞机,具有较好的鲁棒性。     

基于继承PSO算法的动态脑磁共振图像配准

提出了一种用于动态图像配准的混合角点继承PSO算法。该方法采用混合角点检测算子来提取角点,并将继承最优种群的思想引入到PSO优化算法中,即对当前图像配准得到的最优种群进行动态继承与变化后,再用于指导后续图像的配准。实验表明：所提出的算法不仅克服了传统的随机重启方式的脑磁共振图像配准算法中随机设定参数导致配准速度慢的问题,而且提高了图像的配准精度和稳定性。     

人机交互装配中手部指形轮廓信息检测方法

人机交互装配系统中人体手部生物特征的识别与描述是手部装配动作描述与行为理解的基础。在肤色建模与识别、指节特征模型学习的基础上,通过肤色识别结果给出的模板轮廓坐标,计算轮廓链的局部转角比率和逐点向量积,描述基于逐点比较法的指节处的轮廓特征,结合手指生物结构的经验判据,对指尖与指根位置进行检测与推断,实现手指轮廓特征点的初步识别,通过指形拟合完成手指初步定位,为指形平面位姿分析提供基础。在手部多特征检测的基础上进行图像信息融合,结合指节学习模型与自适应阈值识别的指节位置检测,对指形的定位信息进行修正以提高检测的准确性。实验测试结果表明,该手部指形轮廓信息检测方法可行。     

A new approach to corner matching from image sequence using fuzzy similarity index

Corner matching in image sequences is an important and difficult problem that serves as a building block of several important applications of stereo vision etc. Normally, in area-based corner matching techniques, the linear measures like standard cross correlation coefficient, zero-mean (normalized) cross correlation coefficient, sum of absolute difference and sum of squared difference are used. Fuzzy logic is a powerful tool to solve many image processing problems because of its ability to deal with ambiguous data. In this paper, we use a similarity measure based on fuzzy correlations in order to establish the corner correspondence between sequence images in the presence of intensity variations and motion blur. The matching approach proposed here needs only to extract one set of corner points as candidates from the left image (first frame), and the positions of which in the right image (second frame) are determined by matching, not by extracting. Experiments conducted with the help of various sequences of images prove the superiority of our algorithm over standard and zero-mean cross correlation as well as one contemporary work using mutual information as a window similarity measure combined with graph matching techniques under non-ideal conditions.     

星载InSAR非相干图像对精确配准

为了解决星载InSAR非相干图像配准中不同区域对应不同形变参数的问题,将几种传统的SAR图像配准和光学图像配准的方法有机结合,提出了一种基于概略配准、Harris角点检测、相关系数匹配、Delaunay三角网剖分插值映射的InSAR复图像精确配准方法。通过仿真验证,该方法能够适用相干和非相干的InSAR复图像。配准精度达到0.1像素级。     

一个抽取边界曲线特征点的新算法*

景物的特征点抽取是模式识别及计算机视觉中的一个重要问题,已出现的多种检测特征点的方法中主要有角检测法和多边形逼近法。在这两种方法基础之上,人们又提出了结合两种方法的综合方法。提出了一种新的综合方法,首先应用一个简单的角检测方法,然后利用前面计算曲率时的一些值在检测到的角点之间加入一些特征点。实验结果表明新方法比传统方法执行速度更快,并且克服了传统方法的缺陷。     

验证和识别相融合的深度行人识别网络

找到能减小类内距离、增大类间距离的特征表示方法是行人识别的一个挑战。提出一种基于行人验证和识别相融合的深度网络模型来解决这一问题。首先,识别监督学习网络模型增加不同个人的类间间距,验证监督学习网络模型减少同一个行人的类内间距;然后,将行人验证和识别的深度网络融合,提取到更有分辨能力的行人特征向量;最后,采用了联合贝叶斯的行人比对方法,通过监督学习排名的方式,提高行人比对的准确率。实验结果表明,所提方法在VIPeR库上同其他深度网络相比有较高的识别准确率,融合网络与单独的识别和验证网络相比有更高的收敛速度和识别准确率。     

基于特征相似性的RGBD点云配准

三维点云数据的配准是计算机视觉领域的重要研究课题,也是三维重建的关键步 骤。针对 RGBD 点云数据的配准问题,提出一种基于特征相似性的初始配准方法。首先需要计 算待配准的 RGBD 点云模型的曲率和颜色特征度(CFD),并对 CFD 进行统计分析,若模型颜色 特征足够丰富优先采用颜色相似性策略,反之尝试曲率相似性策略。通过特征点提取精简点云 模型,利用确定的对应点选择策略选择候选对应点对。在候选对应点对上采用优化样本一致性 算法获得初始配准变换矩阵,实现两片点云的初始配准。针对不同颜色纹理的 RGBD 点云模型, 本文方法可以自适应选择合适的特征点选择策略,实现点云间良好的初始配准。实验结果表明, 对于几何特征不明显的 RGBD 模型,本文方法能够自适应选择颜色相似性策略来较好地完成初 始配准。对于不同类型的模型配准结果较好,算法效率更高。     

基于模糊形状上下文与局部向量相似性约束的配准算法

非刚性点集配准研究是模式识别领域的一项重要基础研究.本文在当前流行的非刚性点集配准算法的基础上提出了两个主要贡献:1)模糊形状上下文(Fuzzy shape context, FSC)特征;2)基于局部向量特征的局部空间向量相似性约束项.本文首先进行基于特征互补的对应关系评估,在这一步骤中定义了模糊形状上下文特征,然后基于模糊形状上下文特征差异和全局特征差异设计了特征互补的高斯混合模型.其次,进行基于约束互补的空间变化更新.在这一步骤中,定义了局部向量特征,建立了局部空间向量相似性约束项.本文算法通过使用特征互补的高斯混合模型进行对应关系评估,并将配准问题转化为可以用期望最大化(Expectation maximization, EM)算法解决的参数优化问题,通过创建包含局部空间向量相似性约束项的能量方程优化了空间变换更新.本文首先测试了模糊形状上下文特征的检索率,然后采用公开数据集测试了算法在点集配准与图像配准的性能.在与当前流行的十种算法的对比实验中,本文算法均给出了精确的配准结果,并在大部分实验中精度超过了当前流行算法.     

线性拟合联合参数法在图像配准中的应用

提出了一种基于仿透视函数模型和相机响应参数模型共同作用下图像配准算法。首先估算角点匹配以加强匹配精度和速度；由受约束条件控制的分段线性函数近似相机的非线性响应函数，建立起全局参数模型；应用最小二乘法，得到较为精确的参数初始值；最后应用迭代算法降低配准误差。实验结果表明,该算法很好地解决了曝光差异图像配准问题，提高了迭代速度和精确度。