一种基于SFS方法的含高光表面三维重构系统

针对含高光表面三维形状重构的需求,设计了一种基于从明暗恢复形状(SFS)方法的三维重构系统。在正交投影条件下由CCD相机获取点光源照射下的物体表面图像,使用Ward反射模型描述含高光表面的反射特性,建立物体表面图像辐照度方程。系统软件将该方程转化为包含物体高度信息的H-J偏微分方程,并计算此偏微分方程的解,得到物体的高度函数,进而恢复出物体的表面形状。实验表明,该系统可以有效地重构含高光表面的三维形状。     

反求工程中基于图像灰度信息的三维曲面重构

基于物体表面图像灰度信息的三维几何形状反求(由明暗恢复形状),已成为工业界和学术界研究的热点问题之一。首先给出描述该问题的数学物理模型,并利用变分原理将其转化为一个泛函的极小化问题,然后应用有限差分思想和非线性最小二乘方法,将该泛函极小化问题归结为求解线性方程组。最后利用该算法产生的迭代公式对合成图像进行计算,并应用曲面造型软件CAXA(制造工程师)进行曲面重构,从而验证了该方法的有效性和可行性。     

基于SFS技术的纹理力触觉再现方法研究

虚拟现实技术中,纹理的力触觉表达通常需要提取纹理表面的高度轮廓特征,从而再现纹理的凹凸感,而图像纹理则反映的是纹理表面的二维灰度特征.提出了一种根据图像灰度信息恢复表面三维轮廓(shape from shading,SFS),并用于纹理的力触觉表达的方法.采用Tsai & Shah算法从二维纹理图像中恢复出表面三维微观形状,并用力触觉模型渲染.该方法无需专门仪器测量物体表面的微观轮廓,无需设计专用的纹理力触觉表达装置.实验表明该方法是可行的,并通过对实验结果的分析给出了改进方向.     

融合SFS和主动视觉技术的未知物体重建方法

针对未知三维物体自动重建问题,提出一种结合明暗恢复形状的被动视觉和主动视觉技术的新颖三维重建方法.首先利用摄像机分别获取初始位置及旋转180°位置下的未知物体图像信息,采用改进的Tsai-SFS算法恢复物体表面粗糙轮廓,从而获取未知物体的最大尺寸信息.然后结合单目线激光主动视觉系统沿Z轴旋转方向和绕X轴翻转方向上的可视空间模型,预测出物体未知区域信息.最终依据下一视点的可见性判据,将规划过程分为物体侧表面重建规划和上表面重建规划.其中将能获取最大可视曲面面积和最佳表面重建精度的位置定义为下一最优视点位置.并经过实体模型的三维重建验证所提方法的可行性及有效性.     

四自由度准直光源光轴定位方法研究

准直光源用以标定其它光学元件,其准直平行光束指向性确定十分重要.由于准直光源制造安装等因素造成准直光源光轴的初始位置不确定性,需要对准直光源光轴指向定位调整.提出用四自由度机构来实现准直光源光轴定位调整,分析了专用四自由度调整机构的功能和结构.建立四自由度调整机构实现准直光源光轴定位调整数学模型,并进行了解析,确定准直光源光轴定位过程中的量化调整.     

高精度三维物体轮廓测量系统

西安交通大学精密工程研究所研制成功的高精度三维物体轮廓测量系统 ,不久前通过了由陕西省科技厅组织并主持的科技成果鉴定。高精度三维物体轮廓测量系统是以三角测量原理为基础 ,通过出射点、投影点和成像点三者之间的几何成像关系来确定被测物体轮廓各点的高度信息。具体方法是将一线结构光源 (光刀 )投射到被测物体表面 ,由两个对称分布的 CCD摄像机从不同角度获取光刀图像 ,通过图像处理和数据处理的方法得到整个被测表面各点的三维坐标 ,最终将此数据信息形成标准的 CAD/CMA数据文件 ,为产品的进一步设计、开发及数控加工成快速成…     

微小高度检测系统设计

高度信息是分析物体三维形貌的最重要信息。为了研究微小焊点的三维形貌,需要对焊点高度进行测量。针对传统测量技术的缺点,设计了一种检测系统。系统应用白光平行照射条纹板,将其像投影到被测物体上,受到物体高度轮廓的调制作用,形成变形的亮暗相间的条纹。该条纹图携带着焊点的三维信息,再用照相系统把变形的条纹图拍摄在CCD上,通过对CCD上像的处理,可以还原出高度信息。该方法可将条纹板的像等间距地投射到被测物体的表面上,避免了由于投影条纹宽度不同所带了的图像处理过程的误差。实现了测量范围大、重复性好、图像处理简单的优点,为微小高度的测量提供了新方法。     

SMT片式元件焊点三维质量信息提取技术

为实现对表面组装焊点三维质量信息的非接触式提取,提出了一种基于阴影恢复形状原理表面组装片式元件焊点三维质量信息提取方法.为提高重构精度,光照模型采用作者前期研究结果得到的一种改进模型;为解决由阴影恢复形状方法得到的物体三维形态不确定的问题,利用量块得到图像象素值与实际高度的比例关系,以此来确定表面组装焊点的三维形态;采用APDL语言,在ANSYS软件中二次开发建立焊点实体模型,并对相应的焊点体积、剖面面积、润湿角度等三维质量信息进行提取.结合实例介绍了该技术的实现方法与步骤.与采用传统光照模型相比,采用改进光照模型能较好地提高焊点体积信息提取的精度.     

一种用于机器人抓取的单目实时三维重建系统

为支持家庭用服务机器人抓取任务,设计了一种针对家庭环境中常见物体的实时三维重建系统.系统假设相机在围绕物体运动的同时采集图像.首先借助从运动恢复结构算法求解相机在采集图像序列时的运动轨迹.根据运动轨迹中的位置信息,基于几何约束为相机运动轨迹中的每一帧选择最合适的配对图像.提出基于虚拟双目的 立体匹配方法恢复每组配对中两帧图像共视区域的三维结构.利用L-R检查和抛物线拟合等后处理方法剔除误差较大的测点.最后用不同的图像配对计算出的三维结构生成最终的物体模型.实验结果表明生成的物体模型中位数相对误差在1.0％左右,满足机器人抓取任务中对物体模型的精度要求.同时系统平均运行速度超过25帧/秒,满足家庭用物体快速建模的要求.     

一种基于相位法的彩色三维形貌测量方法

基于相位法的三维形貌测量。提出了一种建立被测物体彩色三维形貌的方法。该方法采用主动光源投射一组光强呈余弦变化的光栅。并由一台黑白CCD工业摄像机对被测物体拍摄从而实现三维形貌的重建;然后对被测物体进行三色光投射。同时黑白工业摄像机拍摄被测物体,通过对所得图片进行分析。实现被测物体的色彩恢复。实验结果表明,该方法能够快速准确的获得被测物体的彩色三维形貌,色彩度误差小,可有效解决黑白工业相机无法进行色彩捕捉这一问题。     

Automatic 3D model reconstruction of the diamond object

This paper addresses the problems presented by automatic 3D modeling and measurement in evaluating the diamond-cutting grade of the round brilliant cut diamond. (In this paper, we refer to the round brilliant cut diamond, the most common diamond, unless otherwise specified.) Diamond features such as small size (most are less than 1 cm), lack of texture, highlights, and light transmission intensify these problems, and commonly used methods in computer vision such as laser triangulation, structured light, stereo matching, and shape from shading fail to generate the diamond’s 3D model. Based on the image features of the diamond and the polishing process of the rough diamond, this paper proposes a “shape from multi-view closed contours” (SFMCC) method. First, the diamond is placed on a turntable, and a single CCD camera with one telecentric lens captures silhouette images from multiple views as the turntable rotates. Next, accurate closed contours are extracted from the silhouette images sequence, and a 3D model of the diamond can be retrieved through the space-carving process of the bounding box with multi-view closed contours. To implement the SFMCC method, a complete system including hardware and software is designed and constructed. Many different experiments have been designed to test this method, and results have proven its high efficiency and accuracy.     

Three-dimensional shape reconstruction from images by shape-from-silhouette technique and iterative triangulation

We propose an image-based three-dimensional shape determination system. The shape, and thus the three-dimensional coordinate information of the 3-D object, is determined solely from captured images of the 3-D object from a prescribed set of viewpoints. The approach is based on the shape-from-silhouette (SFS) technique, and the efficacy of the SFS method is tested using a sample data set. The extracted three-dimensional shape is modeled with polygons generated by a new iterative triangulation algorithm, and the polygon model can be exported to commercial software. The proposed system may be used to visualize the 3-D object efficiently, or to quickly generate initial CAD data for reverse engineering purposes, including three dimensional design applications such as 3-D animation and 3-D games.     

基于从明暗恢复形状的雕塑曲面三维形状重构

在从明暗恢复形状问题(由物体二维灰度图象提取其表面高度值的问题)的基本原理和数学模型的基础上,提出了一种求解该问题的算法,利用变分原理和有限差分方法得到了算法的迭代方程。这里将该算法应用于雕塑曲面的三维形状重构,通过对人工合成图象的计算及数控加工实验证明了该算法具有一定的实用价值。     

反求工程中基于图像的汽车零部件曲面的重建

从明暗恢复形状是计算机视觉中的一个重要的研究课题,其目的是利用单幅图像中明暗变化等信息来恢复其表面形状.本文提出的是一种基于汽车零部件曲面图像的三维数据模型重建方法,根据从明暗恢复形状原理实现汽车零部件灰度图像的深度信息提取,通过可视化编程实现建模过程.     

金相显微镜上实现三维微观形貌恢复

金相显微镜是一种经典的测量分析仪器,已经实现了金相显微镜图像的数字化采集,并恢复了三维微观形貌。首先,对金相显微镜的载物台加装电机,利用电机带动载物台进行精密移动,在载物台移动过程中,对被测物体等步距拍摄序列图像。然后,根据聚焦形貌恢复的原理,采用修正的拉普拉斯算子进行聚焦程度判断,利用高斯插值方法获取聚焦程度最大值以及此时被测表面的高度信息,恢复三维形貌。最后,利用金相显微镜对粗糙度样块进行测量,获得了相符的测量结果。该方法对于扩展金相显微镜的测量范围具有积极意义。     

基于DLP数字微镜的结构光三维扫描系统

为了提高现代化生产中的效率和满足三维扫描的需求,设计了一种能够准确提取物件表面三维轮廓信息的扫描系统。系统采用结构光三维成像方法,先通过普通白光将光栅投影到被测物体表面,利用工业相机采集变形光栅图,再根据变形光栅图像中的灰度值变化,用傅里叶变换轮廓法解调出三维坐标信息。实验结果表明,使用傅里叶变换轮廓法重构可获得效果理想的三维点云,其为三维轮廓扫描提供了实验方法,同时也为提高点云精度提供参考。     

Application of convolutional neural network to acquisition of clear images for objects with large vertical size in stereo light microscope vision system

For an object with large vertical size that exceeds the certain depth of a stereo light microscope (SLM), its image will be blurred. To obtain clear images, we proposed an image fusion method based on the convolutional neural network (CNN) for the microscopic image sequence. The CNN was designed to discriminate clear and blurred pixels in the source images according to the neighborhood information. To train the CNN, a training set that contained correctly labeled clear and blurred images was created from an open‐access database. The image sequence to be fused was aligned at first. The trained CNN was then used to measure the activity level of each pixel in the aligned source images. The fused image was obtained by taking the pixels with the highest activity levels in the source image sequence. The performance was evaluated using five microscopic image sequences. Compared with other two fusion methods, the proposed method obtained better performance in terms of both visual quality and objective assessment. It is suitable for fusion of the SLM image sequence.     

Accurate shape from focus based on focus adjustment in optical microscopy

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus–sharpness–is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc.