SFS三维重构技术的发展现状与趋势研究

从明暗恢复形状(SFS)是计算机视觉中三维重构问题的关键技术之一,其原理是利用单幅图像的明暗变化来恢复物体表面的三维形状,应用领域十分广泛.介绍了近些年来SFS方法的发展现状,将SFS算法划分为四种类型:演化和偏微分方程(PDE)方法、最优化方法、局部化方法以及线性化方法.同时,分析了各种SFS算法的特点,并对SFS技...     

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。     

基于虚拟仪器的三维视觉检测系统设计

把计算机视觉领域中的明暗恢复形状(Shape From Shading,简称SFS)技术应用于工业产品的三维视觉检测,利用单幅产品图像的明暗灰度变化来得到物体表面形状信息,并采用全面像素不确定度进行检测分析。该检测系统采用Lab VIEW及其视觉开发工具来实现,可广泛应用于工业自动在线三维视觉形貌检测和质量控制。     

基于Contourlet变换的阴影恢复形貌方法及产品表面缺陷检测应用

为实现对物体表面三维深度缺陷检测,提出一种基于Contourlet变换的明暗恢复形状算法来解决三维物体表面重建问题。针对在传统的明暗恢复形状方法重建过程中出现的计算收敛速度较慢的问题,从图像多尺度几何分析的角度进行研究,利用Contourlet变换系数来表达图像,用基系数近似的计算微分算子,从而优化目标函数的求解过程。试验结果表明使用Contourlet系数的SFS算法比传统和小波系数的算法获得更优的迭代步长,可以使得计算过程中的误差项下降到较低的水平,从而获得更准确的三维信息。检测所使用的三维形状恢复方法只需要根据单幅测量图像,就可实现对零件三维缺陷的检测。     

基于SFS的加工表面三维形貌重建研究

明暗恢复形状(Shape From Shading,SFS)是计算机视觉中一个重要的研究课题,该算法应用于工件表面微观形貌重建,为快速检测表面质量奠定了基础.采用了SFS算法中的最小化方法,介绍了该算法的实现步骤和计算方法.首先利用合成图像对算法的准确度进行了验证,获得了较为准确的重建图像,继而利用工件表面显微图像对工...     

一种非朗伯表面SFS的快速粘性解算法

针对传统的非朗伯表面明暗恢复形状(SFS)算法存在运行时间长、精度不高的问题,提出了一种粘性意义下的明暗恢复形状快速算法。首先,非朗伯物体表面采用Oren-Nayar模型描述其表面反射属性,摄像机镜头使用适合于实际成像过程的透视投影模式,同时假设光源置于镜头像方主点附近,建立了上述情况下的图像辐照度方程,此方程蕴含着非朗伯表面的高度信息。其次,将辐照度方程转化为Hamilton-Jacobi(H-J)类偏微分方程,运用Legendre变换和最优控制理论得到H-J方程对应的Hamilton函数的控制形式。接着,建立了Hamilton函数的逼近算法,使用非线性规划原理构建Hamilton函数最优问题的等价约束问题,利用得到的最优控制并通过Newton法最终得到了H-J方程的粘性解,该粘性解即是非朗伯物体表面的高度。实验结果表明,提出的算法与典型的基于Lax-Friedrichs方法的算法相比,所需要的运行时间大幅度减少,重构的物体表面的高度平均误差与均方根误差也有较大幅度降低。     

基于明暗恢复形状的目标物体深度信息提取算法设计

明暗恢复形状的方法（shape from shading简称SFS）分为全局最小化法、全局演化法、局部分析法、局部线性化法,但这4种方法都有一定的局限性,故在参考局部分析法的基础上,提出了基于明暗恢复形状的目标物体深度信息提取的简单算法。’     

基于透视投影模型的表面三维测量的实现

将明暗恢复形状(SFS)技术应用于表面三维视觉测量.针对传统SFS求解模型的不足,引入一种基于透视投影的反射图模型,并采用高效、简便的灰度梯度算法对该模型进行求解和误差分析.实验结果表明,与正交投影模型相比,透视投影模型的三维表面测量效果显著提高.     

一种基于NL-SFS方法的三维人脸快速重建系统

针对快速重建三维人脸的需求,设计了一种基于非Lambert从明暗恢复形状(NL-SFS)方法的三维人脸快速重建系统。首先人脸表面采用非Lambert反射模型描述其反射特性,这更接近于人脸表面实际的反射特性;接着由遵循正交投影的摄像机获取光源作用下的人脸表面图像,同时假定摄像机方向与光源方向保持一致,建立人脸表面的图像辐照度方程;然后将该方程转化为包含人脸表面深度信息的Eikonal方程;最后根据上述方程的特点,利用fast marching方法设计了系统软件,能够快速求得Eikonal方程的解,进而重建出人脸的三维形状。实验结果表明,该系统可以在较短的时间内获得较高的重建精度,即0.9s内可达到0.43%的高度平均相对误差。     

基于明暗恢复形状的加工表面形貌重构与粗糙度检测

研究了采用明暗恢复形状法(SFS)对加工表面显微视觉图像的三维形貌重构,并实现了表面粗糙度检测.根据金属表面反射特性,采用简化的Oren-Nayar模型与Torrance-Sparrow模型中镜面反射分量叠加的方法,对光照模型进行了改进,并讨论了基于改进后光照模型的SFS最小化计算方法.利用该算法,实现了真实工件表面微...     

Complex wavelet enhanced shape from shading transform for estimating surface roughness of milled mechanical components

The metal surface topology contains abundant information related to the health states of the cutting tool as well as the cutting operation. In this paper, we attempt to adopt 2D digital images of the machined metal surface, acquired via non-contact photo-imaging techniques, as the monitoring media. A Wallis filter based dodging algorithm is applied to cure the uneven contrast phenomenon caused by imperfect lighting illumination. 3D digital models were derived and retrieved from the digital image using a wavelet enhanced Shape from shading (SFS) transform. The minimization based SFS is presented to retrieve the 3D digital surface from the milled workpiece. The dual tree complex wavelet transform is adopted to enhance SFS such that the interfering noise can be suppressed. In the end, quantitative surface roughness indicators are utilized to estimate the surface roughness numerically. A milling cutting experiment of aero-material of aluminum alloy 7075 was carried out to verify the effectiveness of the proposed approach. The comparison results demonstrate that the proposed approach was capable of retrieving 3D surfaces of high precision. With the approach, the digital image emerges as a promising vehicle for machining condition monitoring of CNC machines.

     

基于单幅图像数据的三维重构方法研究

针对传统的基于阴影恢复形状方法（SFS）的三维重构方法的局限性,综合运用计算机视觉、图像处理和计算机辅助几何设计理论与技术,提出了一种实用的三维重构方法。采用SFS方法获取三维形貌信息,利用图像分割技术提取物体的二维轮廓信息,通过三维形貌信息与二维轮廓信息的融合建立物体的数字化模型,通过B样条方法实现数字化模型的重构。实例表明,该方法能够有效地去除图像的背景信息,改善重构模型的分辨力,为基于图像的三维重构技术的广泛应用提供了条件。     

RECONSTRUCTION OF WELD POOL SURFACE BASED ON SHAPE FROM SHADING

A valid image-processing algorithm of weld pool surface reconstruction according to an input image of weld pool based on shape from shading (SFS) in computer vision is presented. The weld pool surface information is related to the backside weld width, which is crucial to the quality of weld joint. The image of weld pool is recorded with an optical sensing method. Firstly, the reflectance map model, which specifies the imaging process, is estimated. Then, the algorithm of weld pool surface reconstruction based on SFS is implemented by iteration scheme and speeded by hierarchical structure. The results indicate the accuracy and effectiveness of the approach.     

A Review of Point Feature Based Medical Image Registration

Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms (PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However, to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of point-feature-based methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.     

A Review of Point Feature Based Medical Image Registration

Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.     

Surface roughness classification using image processing

Surface roughness is an important factor in determining the satisfactory functioning of the machined components. Conventionally the surface roughness measurement is done with a stylus instrument. Since this measurement process is intrusive and is of contact type, it is not suitable for online measurements. There is a growing need for a reliable, online and non-contact method for surface measurements. Over the last few years, advances in image processing techniques have provided a basis for developing image-based surface roughness measuring techniques. Based upon the vision system, novel methods used for human identification in biometrics are used in the present work for characterization of machined surfaces. The Euclidean and Hamming distances of the surface images are used for surface recognition. Using a CCD camera and polychromatic light source, low-incident-angle images of machined surfaces with different surface roughness values were captured. A signal vector was generated from image pixel intensity and was processed using MATLAB software. A database of reference images with known surface roughness values was established. The Euclidean and Hamming distances between any new test surface and the reference images in the database were used to predict the surface roughness of the test surface.