NURBS曲面光顺方法综述

NURBS曲线、曲面的光顺处理是CAD/CAM中非常重要的问题。在研究了NURBS曲面光顺中的几种常用方法的基础上,针对现有光顺算法在多尺度特征并存曲面光顺中的不足,提出利用各向异性小波在表达高维信息的优势,将各向异性小波融入曲面的多分辨率分析中的思想,应用于NURBS曲面光顺,以达到对曲面特征的保存。     

基于广义能量法的截面曲线光顺

对于气动流形曲面,除了满足对原始数据的逼近精度要求之外,通常还要求曲面具有较高的光顺性能。笔者在综合分析现有光顺方法不足的基础上,提出了同时考虑曲面几何及物理等多项光顺准则的基于广义能量法的曲面整体光顺思路,建立了包含曲面截线族粗光顺、曲面精光顺两个步骤的曲面光顺方法。这种方法对于提高曲面的整体光顺性能具有良好的效果。     

基于边缘曲线光顺的图像放大算法

针对常规插值方法在图像放大时图像轮廓模糊的问题,提出了一种基于边缘的图像放大算法。算法包括边缘曲线光顺和插值放大两个步骤。第1步使用能量优化方法,从离散的边缘曲线数据恢复成连续而光顺的边缘曲线,这些曲线将图像分为若干个区域;第2步基于这些光顺曲线,针对不同的区域分别进行图像插值放大计算。使用该方法得到的放大图像边缘清晰光顺,实例证明该算法产生的图像质量高于常规算法。     

曲面光顺的分片能量法

能量法是一种目前被广泛采用的光顺方法,但当数据点较多时,能量法的存储量和计算量都很大。本文研究Ｂ样条曲面光顺的分片能量法。应用该法可以在保持光顺效果的能量法基本一致的前提下,大大地减少了存储量和计算量。实例表明,这是一种行之有效的曲面光顺方法。     

基于最小二乘法的曲面生成算法研究

针对不均匀截面线族曲面生成问题,本文提出了一种基于最小二乘法的曲面生成算法。这种算法在插值的思想上融合了逼近的思想,因此解决了困截面线旅不均匀而使曲面参数线族不合理并导致曲面光顺性不好的问题。该算法已在自行设计的面向数控的曲面造型系统中实现．     

手机曲面玻璃缺陷检测方法研究北大核心CSCD

由于手机曲面玻璃在生产过程中会产生一些外观缺陷,针对曲面玻璃上缺陷成像难、提取难等问题,提出了一种手机曲面玻璃缺陷检测方法。首先,对曲面玻璃平面部分图像进行形状匹配,并对匹配后的图像进行差分和形态学处理,提取缺陷特征;其次,对曲面玻璃曲边部分图像,使用基于连通域分析和面积阈值分割的缺陷提取算法;再次,对曲面玻璃R角部分图像,采用图像频域增强和对数变换的缺陷提取算法;最后,提取各部分缺陷后,计算得到各类缺陷的特征,通过特征进行缺陷分类,并将得到的缺陷数据与影像测量仪得到的数据进行对比实验。结果表明,该算法能准确提取手机曲面玻璃表面常见的划痕、污点、擦伤和气泡缺陷,并且缺陷尺寸精度测量能达到20μm。     

LCD Mura缺陷的B样条曲面拟合背景抑制

针对机器视觉检测TFT-LCD Mura缺陷时存在的图像整体亮度不均匀、背景复杂等影响检测准确性的问题,提出一种基于B样条曲面拟合的背景抑制方法。在最小二乘法准则的约束下,采用双三次B样条曲面拟合算法拟合出背景,并添加光顺项调整拟合精度,用原始图像减去拟合背景,从而消除亮度不均匀背景对缺陷分割造成的影响。为提高算法速度,对原始图像进行分块拟合,并将双三次B样条函数分解为一元函数求解,减小了计算量,同时避免了对原函数求解时容易出现的病态解问题。实验结果表明,该算法准确、高效。     

B-样条曲面的局部形状改进算法

提出了一种双三次B-样条曲面局部形状改进算法。首先根据节点处三阶不连续性的近似局部光顺准则，选择曲面待光顺的节点；然后利用约束的最小二乘逼近法修改相应的局部控制顶点网，从而降低曲面局部的三阶不连续性，使局部形状得到改进。在详细描述算法原理后，给出了算法的实现步骤。     

基于HUBER函数的序列图像位移场估计算法

提出了一种基于Huber函数的序列图像位移场估计算法，与光流法不同的是，它能直接计算位移场，还能在一定程度上减轻对于变化剧烈的“运动边界”过渡平滑的问题，该算法采用共轭梯度法优化能量方程，并利用小波分解进行分级计算，能快速可靠地得到稳定的位移场，实验表明，对有较大弹性形变的图像，与块匹配法相比，该算法得到的平均匹配残差明显减少。     

基于曲率特征混合分类的高密度点云去噪方法

为解决复杂曲面点云在平滑去噪中存在的问题,提出基于曲率信息混合分类的特征保持点云平滑算法．该方法将平面投影与双边滤波算法相结合,采用主成分分析法对点云的局部曲率特性进行评价,使用线性组合混合分类方法将数据分为平面、次特征、富特征类型以及组合类型．针对不同特征邻域类型,提出平面类型的投影平滑方法、次特征和富特征类型的变参数双边滤波法平滑方法的线性组合方法实现点云数据的平滑去噪．将该方法用于激光三维高分辨率人体扫描系统所得到的高密度点云数据,实验结果表明该方法能够在有效光顺点云的同时保持其表面的几何特征,且简化了法向调整的繁杂运算．     

Photometric reconstruction of a dynamic textured surface from just one color image acquisition

Textured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performance of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We thus show that it is possible to have similar results with just one color image.     

Analysis of photometric factors based on photometric linearization

We propose a method for analyzing photometric factors, such as diffuse reflection, specular reflection, attached shadow, and cast shadow. For analyzing real images, we utilize the photometric linearization method, which was originally proposed for image synthesis. First, we show that each pixel can be photometrically classified by a simple comparison of the pixel intensity. Our classification algorithm requires neither 3D shape information nor color information of the scene. Then, we show that the accuracy of the photometric linearization can be improved by introducing a new classification-based criterion to the linearization process. Experimental results show that photometric factors can be correctly classified without any special devices. A further experiment shows that the proposed method is effective for photometric stereo.     

Joint blind restoration and surface recovery in photometric stereo

We address the problem of simultaneous estimation of scene structure and restoration of images from blurred photometric measurements. In photometric stereo, the structure of an object is determined by using a particular reflectance model (the image irradiance equation) without considering the blurring effect. What we show is that, given arbitrarily blurred observations of a static scene captured with a stationary camera under different illuminant directions, we still can obtain the structure represented by the surface gradients and the albedo and also perform a blind image restoration. The surface gradients and the albedo are modeled as separate Markov random fields, and a suitable regularization scheme is used to estimate the different fields as well as the blur parameter. The results of the experimentations are illustrated with real as well as synthetic images.     

基于四光源光度立体法的药品泡罩包装中 铝箔缺陷检测方法

为了提高药品泡罩包装中铝箔缺陷检测效率,提出了基于四光源光度立体法的铝箔缺陷检测方法。利用四光源光度立体法可以高效、准确地获得待测对象的表面梯度信息,接着使用表面梯度信息得到待测对象的表面高斯曲率,再对表面高斯曲率图进行形态学处理,最后从灰度图像中有效分割铝箔缺陷区域。实验结果表明,本方法可以有效地检测铝箔缺陷,缺陷检测的准确率在91%以上。     

Studying vortex flame structure by means of digital photometry

The application of a method of digital image processing to the visualization of vortex flows and calculation of the temperature and concentration fields in a flame is described. Photographs of the reverse flame glow and its photometric images are obtained and it is established that the photometric images are identical to their interference counterparts. The main advantages of the digital photometry technique are the simplicity of the procedures and the low cost of the instrumentation. Results of the temperature field calculation for an axisymmetric reverse flame are presented.     

3D reconstruction of concave surfaces using polarisation imaging

This paper presents a novel algorithm for improved shape recovery using polarisation-based photometric stereo. The majority of previous research using photometric stereo involves 3D reconstruction using both the diffuse and specular components of light; however, this paper suggests the use of the specular component only as it is the only form of light that comes directly off the surface without subsurface scattering or interreflections. Experiments were carried out on both real and synthetic surfaces. Real images were obtained using a polarisation-based photometric stereo device while synthetic images were generated using PovRay® software. The results clearly demonstrate that the proposed method can extract three-dimensional (3D) surface information effectively even for concave surfaces with complex texture and surface reflectance.     

Specularity and shadow detection for the multisource photometric reconstruction of a textured surface

Textured surface analysis is essential for many applications. In this paper, we present a three-dimensional (3D) recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to reconstruct the textured surfaces in 3D with a high degree of accuracy. For this, the proposed method uses a sequence of six images and a Lambertian bidirectional reflectance distribution function (BRDF) to recover the surface height map. A hierarchical selection of these images is employed to eliminate the effects of shadows and highlights for all surface facets. To evaluate the performances of our method, we compare it to other traditional photometric stereo methods on real textured surfaces using six or more images.     

Color from shape from color: a simple formalism with known light sources

Photometric stereo is a well-known technique for recovering surface normals of a surface but requires three or more images of a surface taken under illumination from different directions. At best, one may dispense with the need for multiple images by using colored lights tuned to camera filters. But a less restrictive paradigm is available that uses the orientation-from-color approach, wherein multiple broadband illuminants impinge on a surface simultaneously. In that method, colors for a Lambertian surface lie on an ellipsoid in color space. The method has been applied mainly to single-color objects, with ellipsoid quadratic-form parameters determined from a large number of pixels. However, recently Petrov and Antonova [Color Res. Appl. 21, 97 (1996)] developed an entirely local approach, useful also for multicolored objects with color uniform in each patch. We investigate to what extent a method such as that of Petrov and Antonova can be applied in the ostensibly simpler situation in which the complex lighting environment is known, i.e., a color photometric stereo situation, with all lights in play at once with only a single image to analyze. We find that, assuming a simple model of color formation, we are able to recover the object colors along with surface normals, using only a single image. Because we immerse the object in a known lighting environment, we show that only half of the equations utilized by Petrov and Antonova are actually needed, making the method more stable. Nevertheless, solutions do not exist at every pixel; instead we may determine a best estimate of patch color, using a robust estimator, and then apply that estimate throughout a patch. Results are shown to be quite good compared with ground truth. The simple color model can often be made to hold more exactly by transforming the color space into one corresponding to spectrally sharpened sensors, which are a matrix transform away from the actual camera sensors. In our study the reliability and accuracy of the normal vector and of the surface color recovery algorithm are improved by this straightforward transformation.