Sub-pixel extraction of laser stripeand itsapplication in laser plane calibration

For calibrating the laser plane to implement 3D shape measurement, an algorithm for extracting the laser stripe with sub-pixel accuracy is proposed. The proposed algorithm mainly consists of two stages： two-side edge detection and center line extraction. First, the two-side edge of laser stripe is detected using the principal component angle-based progressive probabilistic Hough transform and its width is calculated through the distance between these two edges. Secondly, the center line of laser strip is extracted with 2D Taylor expansion at a sub-pixel level and the laser plane is calibrated with the 3D reconstructed coordinates from the extracted 2D sub-pixel ones. Experimental results demonstrate that the proposed method can not only extract the laser stripe at a high speed, nearly average 78 ms/frame, but also calibrate the coplanar laser stripes at a low error, limited to 0.3 mm. The proposed algorithm can satisfy the system requirement of two-side edge detection and center line extraction, and rapid speed, high precision, as well as strong anti-jamming.

Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model （SPSAM）, are described by the modified SPSAM （MSPSAM） that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model （MSAM） for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM （MPS）, MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Sub-pixel high precision dimensional measurement method for aero-engine hollow turbine blade based on industrial computed tomography image

Aero-engine hollow turbine blades are work under prolonged high temperature,requiring high dimensional accuracy.Blade profile and wall thickness are important parameters to ensure the comprehensive performance of blades,which need to be measured accurately during manufacturing process.In this study,a high accuracy industrial computed tomography(ICT)measuring method was developed based on standard cylindrical pin and ring workpieces of different sizes.Combining ICT with cubic spline interpolation,a sub-pixel accuracy was achieved in measuring the dimension of component.Compared with the traditional and whole-pixel level image measurement method,the cubic spline interpolation algorithm has the advantages of high accuracy in image edge detection and thus high accuracy of dimensional measurement.Further,the technique was employed to measure the profile and wall thickness of a typical aerospace engine turbine blade,and an accuracy higher than 0.015 mm was obtained.

Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage:A Case Study from Beijing Municipality,China

The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.

Sub-pixel mapping method based on BP neural network

A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

Fast Approaches for Sub-pixel Precision Variable Block Size Motion Estimation

The new features of H. 264 video coding standard make the motion estimation module much more time consuming than before. Especially, the motion search is required for each of the 4 modes for inter prediction. In order to reduce the computational complexity, we analyze the statistics of results of motion estimation, such as the continuity of best modes of blocks in successive frames and the chance to give up a sub-partition mode （smaller than 16 × 16） after integer-pixel motion estimation, from which we suggest to make mode prediction based on the motion information of the previous frame and skip sub-pixel motion estimation in subpartition mode selectively. According to the experimental result, the proposed algorithm can save 75 % of the computational time with a slight degradation （0.03 dB） on PSNR compared with the pseudocode of fast search motion estimation in JM12.2.

Fast Sub-Pixel Motion Estimation Algorithm For H.264

A novel fast sub-pixel search algorithm is proposed to accelerate sub-pixel search. Based on the features of predicted motion vector (PMV) and texture direction observed, the proposed method effectively filters out impossible points and thus decreases 11 searched points in average during the sub-pixel search stage. A threshold is also adopted to early terminate the sub-pixel search. Simulation results show that the proposed method can achieve up to 4.8 times faster than full sub-pixel motion search scheme (FSPS) with less than 0.025 dB PSNR losses and 2.2% bit-length increases.

Sub-pixel edge detection method for miniature parts in microscopic field of view

In order to improve the edge detection precision of miniature parts in microscopic field of viewa sub-pixel edge detectionalgorithm combining non-orthogonal quadratic B-spline wavelet transform algorithm and Zernike moment algorithm is proposed.Non-orthogonal quadratic B-spline wavelet transform algorithm is adopted to get the pixel edge of miniature parts?andthe moment invariant of Zernike moment algorithm is used for refining the pixel edge to get sub-pixel edges.A real-time detectionsystem based on the proposed algorithm for miniature parts is established.The general system structure and operational principle are given,the real-time image acquisition and detection are completed,the results of edge detection are analyzed and the detection precision is evaluated.The results show that parts size can be0.01-10mm and the detection precision reaches0.01%-0.1%.Therefore,the edge of the miniature parts can be accurately identified and the detection precision can be improved to sub-pixel level,which meets the requirements of miniature parts precision detection.

基于机器视觉的木窗双端铣削加工尺寸测量方法

木窗是一种以木材或木质复合材料为主要构件的门窗产品,具有良好的生态性能和美观效果,适用于多种建筑形式和风格,其中木窗尺寸是衡量木窗加工是否合格的重要指标。对于传统木窗双端铣削加工中人工测量尺寸方式存在的精度低、效率低等问题,提出一种基于机器视觉的木窗双端铣削加工尺寸测量方法,以期提高尺寸测量精度及加工效率。该方法针对木窗厚度引起的透视效应,提出一种物平面提升法,以消除透视投影带来的误差。首先对木窗图像采取灰度化、平滑去噪、图像增强及轮廓分割等举措,完成图像预处理,提取出木窗内外轮廓区域。对轮廓区域应用Canny算子获取木窗像素级轮廓。通过优化的Zernike矩亚像素边缘提取算法对木窗像素级边缘进行更精确的定位,得到亚像素级轮廓坐标。通过最小二乘法联合RANSAC算法对亚像素轮廓坐标进行拟合,得到拟合轮廓及角点坐标,并使用透视矫正模型计算出木窗尺寸。实验利用3种厚度规格相同但尺寸不同的松木材质矩形木窗,分别测量其内框和外框的边框尺寸及对角线尺寸,并与对应的实际物理尺寸对比,验证了所提木窗尺寸测量方法的检测精度。研究结果表明,所提方法与实际物理尺寸值相比,其绝对误差范围在±0.12 mm之内,相对误差在±0.1%之内,且效率及精度高,可以满足对木窗的在线尺寸检测。

基于优化Hough变换的铆接高度差亚像素检测方法研究

针对飞机铆接高度差的检测问题,提出了一种优化的Hough变换铆接高度差亚像素检测方法。该方法首先通过空间域点运算的灰度变换法对采集的铆接孔图像进行增强处理,然后利用局部阈值分割法进行图像分割,采用Canny算法进行边缘粗提取,再利用优化的Hough变换进行亚像素级的边缘精细提取,提取出铆接孔和铆钉钉头的圆环区域,最后结合RANSAC算法进行圆拟合,利用开发算子get_current_region_z()分别提取内外圆环区域的高度平均值,再通过函数height_Z()将所得的高度平均值作差即可得到铆接表面的高度差。经试验证明,该检测方法亚像素精确定位能力强,检测结果准确率高、稳定性好,重复测量精度可达到±10μm。

基于Lagrange插值的数字图像相关亚像素位移测量算法

本文旨在通过改进经典Newton-Raphson算法中的插值方法来优化数字图像相关(DIC)亚像素位移测量的计算效率。针对经典Newton-Raphson算法在数字图像相关亚像素位移测量中应用的双三次样条插值算法,特别是在需要计算边界点灰度梯度(即灰度值的一阶导数)时,存在计算量大的问题,本文提出一种基于Lagrange插值的改进算法作为解决方案。Lagrange插值算法在未增加计算节点数的情况下,成功避免了边界点灰度梯度的计算,使插值效率可提高近1/3。应用数值模拟实验和真实刚体平移实验对本文算法的性能进行验证。结果表明,本文算法在保持求解精度的同时,计算测量效率较传统Newton-Raphson算法有了明显提升。单点插值效率平均提升近30%;单点测量效率平均提升近20%。本文首次将Lagrange插值算法应用于数字图像相关的亚像素位移测量中,解决了双三次样条插值算法计算量大的问题,为相关领域提供了一种更为优化的变形测量方法。该研究为DIC亚像素位移测量技术的进一步发展和应用提供了新的思路和参考,有望在实际工程中得到广泛应用,促进相关领域的进步与发展。

基于机器视觉的电芯绝缘介质定位算法

为保证电芯绝缘介质定位的准确性和时效性,文章提出一种基于双线性插值亚像素坐标结合改进随机抽样一致性(random sample consensus,RANSAC)算法的绝缘介质定位算法。对工业采集的图像进行空间滤波、阈值分割等预处理操作,分割出目标并增强特征;运用Canny边缘检测算子检测边缘,选取定位轮廓并根据最小外接矩形分离4条边缘直线,用双线性插值公式精确定位边缘直线亚像素坐标;采用部分点先验模型约束RANSAC算法提取4条边缘直线的高质量内点,再用最小二乘法分别拟合4条边缘直线并计算出相应偏移量。实验结果表明,该算法能有效保证绝缘介质的定位精度且具有一定的时效性,可以较好地满足实际生产应用的要求。

Vibration-assisted vat photopolymerization for pixelated-aliasing-free surface fabrication

Mask image projection-based vat photopolymerization(MIP-VPP)offers advantages like low cost,high resolution,and a wide material range,making it popular in industry and education.Recently,MIP-VPP employing liquid crystal displays(LCDs)has gained traction,increasingly replacing digital micromirror devices,particularly among hobbyists and in educational settings,and is now beginning to be used in industrial environments.However,LCD-based MIP-VPPsuffers from pronounced pixelated aliasing arising from LCD’s discrete image pixels and itsdirect-contact configuration in MIP-VPP machines,leading to rough surfaces on the 3D-printed parts.Here,we propose a vibration-assisted MIP-VPP method that utilizes a microscalevibration to uniformize the light intensity distribution of the LCD-based mask image on VPP’s building platform.By maintaining the same fabrication speed,our technique generates asmoother,non-pixelated mask image,reducing the roughness on flat surfaces and boundary segments of 3D-printed parts.Through light intensity modeling and simulation,we derived an optimal vibration pattern for LCD mask images,subsequently validated by experiments.We assessed the surface texture,boundary integrity,and dimensional accuracy of componentsproduced using the vibration-assisted approach.The notably smoother surfaces and improved boundary roughness enhance the printing quality of MIP-VPP,enabling its promisingapplications in sectors like the production of 3D-printed optical devices and others.

基于机器视觉的闸坝表面位移非接触式监测方法

针对闸坝表面位移常规监测方法劳动密度高、监测频次低,且难以实现长期稳定监测等问题,提出一种融合时空特征的闸坝表面位移非接触式智能监测方法。该方法采用人工靶标作为标志物,以摄像机为采集设备,通过无线传输图像信息,利用加权分布的自适应伽马修正(AGCWD)与边缘感知因子改进的加权引导滤波(WGIF)增强低照度图像的特征表达能力,通过计算机搭载基于贝叶斯框架的时空上下文信息(STC)算法深度挖掘靶标图像上下文时空信息,进一步地,引入曲面拟合获取靶标的亚像素级位移时程信息,实现闸坝水平和垂直双向表面位移的亚像素级非接触式监测。实验室与现场试验结果表明,不同实验场景下位移监测数据与校验数据高度一致,误差小于0.05 mm;相比于图像优化处理方法,基于AGCWD与WGIF的图像优化处理方法的峰值信噪比提升2.70%,信息熵增加4.91%,标准差降低2.63%;相比于目标追踪算法,基于曲面拟合的STC目标追踪算法的现场监测数据较同类目标追踪算法精度提升48%,可为闸坝表面位移监测提供高精度的解决方案。

基于亚像素精度的汽车冲压件尺寸测量系统设计

针对汽车冲压件尺寸人工测量方式存在的检测效率低和测量精度难以保证等问题,提出了一种基于亚像素精度的尺寸测量方法。先对汽车冲压件进行图像采集和预处理,接着利用Canny算子和改进Zernike矩进行冲压件图像亚像素边缘提取,再通过Hough变换对提取的圆轮廓进行拟合,最终获得了冲压件外圆直径和3个内孔直径的精确尺寸,4个圆轮廓的测量精度均高于0.025 mm,且多次测量结果的标准差小于0.009 6 mm。基于亚像素精度的尺寸测量系统,完成了测量平台的搭建和软件系统的开发,实现了汽车冲压件尺寸的非接触式亚像素级精度测量,能够满足工业应用的快速连续性测量要求。

共形天线表面微小焊盘视觉定位算法设计

共形天线是一种特殊形态的新型雷达天线,具有低剖面性以及低散射面积,是未来雷达天线发展的重要趋势之一。当前,共形天线的装配方式正由人工装配转向自动化装配以提升生产效率和批产一致性,但共形天线表面的焊盘尺寸小,数量多且分布在复杂曲面上,现有视觉定位算法难以满足实际使用需求。为此,文中提出了一种多目标亚像素定位算法。该算法结合了模板匹配和亚像素定位算法,可以同时给出多个焊盘的精准位置信息。由仿真分析与实验验证可知,该算法的视觉定位精度可达5μm,有效提升了共形天线的装配质量。

基于亚像素定位的图像边缘检测策略研究

针对图像处理与计算机视觉技术中低对比度、边缘模糊图像的边缘检测问题,参考局部极值与梯度方向两种因素,并结合图像边缘方向趋势,提出了一种单像素边缘跟踪策略。相较于应用广泛的Canny算法,该跟踪策略无需设置全局阈值,实现方式更为简洁、高效;提取的图像边缘连续、平滑、完整,并有效地减少了图像边缘的冗余像素,进而提升了图像后续处理的效率;边缘跟踪方向抗干扰性强,具有较强的鲁棒性。为了减小检测的图像边缘与真实图像边缘之间的偏差、提高图像边缘检测的精度,参考边缘像素点的相邻区域灰度,以边缘像素点的梯度分布为依据对该像素点进行亚像素定位。经实验验证,经过亚像素优化的图像边缘检测策略可用于检测边缘模糊、对比度低的图像,检测的图像边缘完整、连续且平滑。该策略有效地消除了程序运算中引入的截断误差,提升了图像边缘检测精度,且适用于亮度5~100000 lx的高动态成像场景中。

基于数字图像相关法的轮轨力测量研究

针对应变计式轮轨力测量方法的不足,研究一种基于数字图像相关法的非接触式轮轨力测量方法。通过受力分析以及有限元计算,确定钢轨表面非接触测点区域位置。根据轨旁检测的特点设计测量系统的整体方案并分析了其测量原理,对相机进行了分析选型,以实现轮轨力作用下钢轨表面测点区域变形散斑图像的高质量采集。基于数字图像相关法对所采集的变形散斑图像进行分析,采用1阶形函数描述钢轨的变形,并使用零均值归一化差值平方和函数作为相似度的度量指标;为了提高系统的测量分辨精度,在整像素匹配算法的基础上,通过对位移初值进行迭代计算进一步得到亚像素级位移,并使用最小二乘法拟合位移场结果得到相应的应变场。在实验室设计并搭建了具有移动加载功能的轮轨力测量试验系统;通过静态标定试验准确建立了反映钢轨应变和轮轨垂向力大小数学关系的线性标定曲线,并分析讨论了相机工作距离和光照强度等因素对轮轨力测量的影响;在移动载荷作用下进行了轮轨力动态测量试验,与接触式应变计法测量结果进行比较,非接触式数字图像相关法在跨中位置的最大轮轨力测量误差不超过0.3%。动静态测量试验结果表明,数字图像相关法能够实现轮轨力的精确测量,是一种有效的非接触式轮轨力测量方法。

手机OLED屏幕子像素排布的观察与颜色合成实验

三基色原理是彩色显示器件的基本原理之一,颜色合成实验是物理实验体系中体现三基色原理的常见实验。将实验内容与日常生活紧密结合,激发学生的学习兴趣,是提升实验教学效果的有效手段。学生自己的手机作为实验仪器设计的手机OLED屏幕子像素分布的观察与颜色合成实验能够在完成颜色相加混合实验的同时,调动学生的探究热情,实现良好的教学效果。与现行实验相比,开展实验不需要额外光源和颜色合成仪,成本大幅降低,具有良好的推广意义。

基于改进Zernike矩的轴类零件圆度误差视觉评定

针对轴类零件圆度误差评定普遍存在的效率低、精度差等问题,提出了一种基于改进Zernike矩的轴类零件圆度误差视觉评定方法。首先,搭建轴类零件视觉检测平台,在位移平台控制下拍摄不同截面图像,并利用棋盘格标定法进行相机标定,校正畸变和计算像素当量。其次,以Sobel算子为基础,通过三灰度边缘过渡模型提高测量精度,利用大津法确定最佳阈值,改进Zernike矩亚像素检测方法。最后,使用改进的PSO算法处理轮廓关键坐标点进行圆度误差评定,并与三坐标测量机测量实验。实验结果表明,圆度误差视觉评定方法与CMM测量结果误差在3μm以内,且稳定性较好,在圆度误差评定中具有实际可行性。