Robust active stereo vision using Kullback-Leibler divergence

Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.     

Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object‐oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object‐oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey‐level co‐occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object‐oriented classification of urban areas using very high resolution imagery.     

一种保持纹理的网面简化算法

在计算机视觉领域,三维网面的简化不仅要求保持物体形状和拓扑关系,还要求保持物体表面法线,纹理,颜色和边缘等物体特征,以使计算机视觉系统能有效地表示,描述,识别和理解物体和场景,为此讨论了一种基于边操作（边收缩,边分裂）,并具有颜色或灰度纹理特征保持的三维网面的简化算法,该算法将网面不对称最大距离作为形状改变测度,将邻域内颜色或灰度最大改变量作为纹理改变测试,从而在大量简化模型数据的同时,有效地保持了模型的几何形状,拓扑关系,颜色或灰度特征,以及网面顶点均匀分布。     

三维视觉测量技术及应用进展

三维视觉测量是计算机视觉与精密测量原理交叉融合的前沿高新技术,是工业4.0的基础支撑,是以网络化、智能化制造为变革特征的先进制造业的核心关键技术。经过几十年的发展,三维视觉测量技术在基础研究和应用研究上均获得了快速深入发展,形成了理论方法、技术工艺、系统研发和产品应用四位一体较为完备的方向体系,呈现出理论系统化、方法多维化、精度精准化和速度快捷化的发展趋势,成为智能制造过程控制、产品质量检验保证和装备整机服役测试的不可或缺的优选技术。本文主要围绕单相机、双相机和结构光等典型三维视觉测量技术展开论述,概要介绍其关键技术内涵,综述其发展现状、前沿动态、热点问题和发展趋势。重点论述条纹投影三维测量技术和相位测量偏折术。最后给出了三维视觉测量的发展趋势与未来展望。     

Two-Part Texture Mappings

Most published techniques for mapping two-dimensional texture patterns onto three-dimensional curved surfaces assume that either the texture pattern has been predistorted to compensate for the distortion of the mapping or the curved surfaces are represented parametrically. We address the problem of mapping undistorted planar textures onto arbitrarily represented surfaces. Our mapping technique is done in two parts. First the texture pattern is embedded in 3-space on an intermediate surface. Then the pattern is projected onto the target surface in a way that depends only on the geometry of the target object (not on its parameterization). Both steps have relatively low distortion, so the original texture need not be predistorted. We also discuss interactive techniques that make two-part mapping practical.     

一种基于多分辨率与模糊聚类技术的散焦图像分割算法

带有投射光栅的散焦图像的准确分割是3维物体复原的重要环节。为了更准确地进行散焦图像分割，基于所提取的带光栅散焦图像特征，提出了一种将多分辨率分析与模糊聚类技术融合实现图像分割的算法。该算法是利用多分辨率技术来建立面向像素特征向量的多级图像联系矩阵，并在图像低分辨率级进行基于模糊聚类的区域分割。实验证明，该技术不仅克服了直接分割的困难，而且提高了分割的正确率，因此表明，该算法是有效的。     

Reconstruction of 3D contour with an active laser‐vision robotic system

This paper presents a 3D contour reconstruction approach employing a wheeled mobile robot equipped with an active laser‐vision system. With observation from an onboard CCD camera, a laser line projector fixed‐mounted below the camera is used for detecting the bottom shape of an object while an actively‐controlled upper laser line projector is utilized for 3D contour reconstruction. The mobile robot is driven to move around the object by a visual servoing and localization technique while the 3D contour of the object is being reconstructed based on the 2D image of the projected laser line. Asymptotical convergence of the closed‐loop system has been established. The proposed algorithm also has been used experimentally with a Dr Robot X80sv mobile robot upgraded with the low‐cost active laser‐vision system, thereby demonstrating effective real‐time performance. This seemingly novel laser‐vision robotic system can be applied further in unknown environments for obstacle avoidance and guidance control tasks. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于摄影原理的三维纹理重建技术研究

讨论的是基于摄影原理的三维物体表面纹理的重建技术.该技术从国内外研究现状和主要不足出发,对三维物体表面纹理的采集和映射方法进行研究和改进,提出了较为合理的算法.纹理采集过程中分析了回转工作台的结构、纹理采集的原则并建立相关的坐标系;根据拍照和投影变换的原理建立三个坐标系对应点间的关系、推导出坐标变换关系式和转换矩阵;对模型表面微小面片法向量进行均匀化处理;通过计算面片的法向量与纹理采集方向向量的位置关系确定其最佳纹理,最后通过多角度映射达到三维物体表面纹理重建的目的.     

3D Euclidean versus 2D non-Euclidean: two approaches to 3D recoveryfrom images

Methods of 3D recovery in computer vision for computing the shape and motion of an object from projected images when an object model is available are classified into two types: the 3D Euclidean approach, which is based on geometrical constraints in 3D Euclidean space, and the 2D non-Euclidean space. Implications of these two approaches are discussed, and some illustrating examples are presented     

基于纹理分析的表面粗糙度等级识别

提出了一种利用图象纹理分析技术进行机械加工表面粗糙度检测的非接触检测方法,该方法首先根据统计方差对待测工件的表面粗糙度进行粗分类,然后,利用基于Ｇａｂｏｒ滤波器的纹理分类器,识别待测工件表面粗糙度等级。该新方法可简单、快速地实现表面粗糙度等级的自动识别,而且对图象旋转具有不变性,由于其纹理分类器的参数少,并且新方法成本低,参数标定方便,因而便于现场检测,如果与机床的控制系统相连,还可以实现加工的实     

3D object model reconstruction from image sequence based on photometric consistency in volume space

This paper presents a system that can reconstruct a photorealistic 3D object model from an image sequence captured at arbitrary viewpoints. The whole system consists of four steps: camera calibration, volumetric modeling, polygonal model formation and texture mapping. We adopt the shape-from-silhouette approach for volumetric modeling. There are two common types of object surface that are difficult to reconstruct—textureless surface and concave surface. To tackle the problems, we propose to perform the volumetric modeling based on the constraints of viewpoint proximity and photometric consistency in the volume space. The volumetric model is converted to the mesh model for efficient manipulation. Finally, the texture map is generated from the image sequence to give the 3D model a photorealistic appearance. Some reconstructed object models are presented to demonstrate the superior performance of our system as compared with the conventional modeling technique based on the photo-consistency in the image space.     

Flexible calibration of structured-light systems projecting point patterns

Structured-light systems (SLSs) are widely used in active stereo vision to perform 3D modelling of a surface of interest. We propose a flexible method to calibrate SLSs projecting point patterns. The method is flexible in two respects. First, the calibration is independent of the number of points and their spatial distribution inside the pattern. Second, no positioning device is required since the projector geometry is determined in the camera coordinate system based on unknown positions of the calibration board. The projector optical center is estimated together with the 3D rays originating from the projector using a numerical optimization procedure. We study the 3D point reconstruction accuracy for two SLSs involving a laser based projector and a pico-projector, respectively, and for three point patterns. We finally illustrate the potential of our active vision system for a medical endoscopy application where a 3D cartography of the inspected organ (a large field of view surface also including image textures) can be reconstructed from a video acquisition using the laser based SLS.     

单像机有源形状恢复方法研究

物体表面三维形状恢复是计算机视觉的一个重要研究内容.给出了一种利用网格结 构光,采用单像机恢复物体表面三维形状的方法.提出了一种新的定标方法和投影模板检测 算法,并构造完成了有源三维重建实验系统.结果表明该方法能快速、准确地恢复物体形状.     

Manual/automatic colorization for three-dimensional geometric models utilizing laser reflectivity

While laser scanners can produce a high-precision 3D shape of a real object, appearance information of the object has to be captured by an image sensor, such as a digital camera. This paper proposes a novel and simple technique for colorizing 3D geometric models based on laser reflectivity. Laser scanners capture the range data of a target object from the sensors. Simultaneously, the power of the reflected laser is obtained as a by-product of the range data. The reflectance image, which is a collection of laser reflectance depicted as a grayscale image, contains rich appearance information about the target object. The proposed technique is an alternative to texture mapping, which has been widely used to realize photo-realistic 3D modeling but requires strict alignment between range data and texture images. The proposed technique first colorizes a reflectance image based on the similarity of color and reflectance images. Then the appearance information (color and texture information) is added to a 3D model by transferring the color in the colorized reflectance image to the corresponding range image. Some experiments and comparisons between texture mapping and the proposed technique demonstrate the validity of the proposed technique.     

Articulated body motion tracking using illumination invariant optical flow

We propose a model-based tracking method for articulated objects in monocular video sequences under varying illumination conditions. The tracking method uses estimates of optical flows constructed by projecting model textures into the camera images and comparing the projected textures with the recorded information. An articulated body is modelled in terms of 3D primitives, each possessing a specified texture on its surface. An important step in model-based tracking of 3D objects is the estimation of the pose of the object during the tracking process. The optimal pose is estimated by minimizing errors between the computed optical flow and the projected 2D velocities of the model textures. This estimation uses a least-squares method with kinematic constraints for the articulated object and a perspective camera model. We test our framework with an articulated robot and show results.     

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets.     

Recognition of quadratic surface of revolution using a robotic vision system

Reverse engineering using 3D scanners has been gaining increasing popularity. One challenging task that remains is to recognize the geometric feature from the cloud data scanned. In this study, a robotic vision system is used to recognize quadratic surfaces of revolution on an object.The top-view image of an object is used to detect the surface boundary by loop analysis technique. The boundary of a single surface is extracted according to the 2D loop of that surface. The robot then projects laser lines through the principal axes of the loop to get the sectional curves. The surface is recognized by a curve-fitting method based on the characteristics of these curves.This study provides a simple and faster method to detect the manufacture features on an object that contains quadratic surfaces. The data structure can be output in IGES format for re-design or rapid manufacture of the object.     

A haptic-rendering technique based on hybrid surface representation

A novel haptic rendering technique using a hybrid surface representation addresses conventional limitations in haptic displays. A haptic interface lets the user touch, explore, paint, and manipulate virtual 3D models in a natural way using a haptic display device. A haptic rendering algorithm must generate a force field to simulate the presence of these virtual objects and their surface properties (such as friction and texture), or to guide the user along a specific trajectory. We can roughly classify haptic rendering algorithms according to the surface representation they use: geometric haptic algorithms for surface data, and volumetric haptic algorithms based on volumetric data including implicit surface representation. Our algorithm is based on a hybrid surface representation - a combination of geometric (B-rep) and implicit (V-rep) surface representations for a given 3D object, which takes advantage of both surface representations.     

基于CV／CAD的三维物体几何建模

在虚拟现实等技术领域中,都涉及到由现实世界中的实际景物建立对应的计算机描述的虚拟景物的问题,为此提出了利用计算机视觉与CAD几何建模技术相结合的三维珠体建模途径,首先通过编码光栅方法获取三维物体的深度图象,并采用数学形态学的方法加以分割,然后利用代数曲面拟合手段对分割后的三维曲面片进行重建,并使用CAD几何建模工具由重建的曲面片构成物体的几何模型,该文给出了初步的实验结果,证明所提出的技术途径基本可行。