A high-speed 3-D image measurement method

With the recent popularization of digital cameras and projectors, practical applications of three-dimensional (3-D) image measurements based on intensity-modulation pattern projection are eagerly anticipated. Such an approach would permit a projection pattern to contain more measurement data, allowing 3-D data to be calculated with greater accuracy. However, to achieve a high level of accuracy using this approach, it is essential that an ideal observation pattern image should be obtained with a certain minimum number of stripes and a certain intensity distribution. To satisfy these requirements, an intensity correction method with two observation pattern images has previously been used. It is difficult to measure a fast-moving object in such calculations because the correspondence relation of the measured object is not established between the two photographic images, and consequently the pattern intensity is not corrected accurately. This study proposes an image analysis method for correcting intensity using an image synthesis technique that extracts precise stripes from a single observation pattern image. This analysis method allows 3-D shape measurements to be performed from a single projection pattern and a single image capture.     

3-D face recognition method based on optimum 3-D image measurement technology

Most face recognition systems employ 2-D color or gray-scale images. However, face recognition based on 2-D images is adversely affected by 3-D movement, variable lighting, and the use of cosmetics. 3-D image measurement technology has the potential to overcome these limitations of face recognition based on 2-D images since it can perform geometric analysis. We propose a method that is capable of recognizing a person from a 3-D facial image obtained using a 3-D shape measurement system by employing a technique that optimizes the intensity-modulation pattern projection. This face recognition method is based on the iterative closest point algorithm. It is robust to changes in reflectivity and color. Since the 3-D facial information can be registered, this method can estimate rotations and translations to compensate for different positions or directions. In order to prove the validity of the proposed technique, a verification experiment was conducted which used 105 sample 3-D images obtained from 15 subjects. It achieved a detection rate of 96% when heads were turned at an angle of 20° or less relative to the camera.     

基于VTK的医学图像交互式三维测量技术研究

VTK是国外开发的集计算机图形.图像处理和可视化于一体的工具开发包,具有高度的灵活性、可移植性.提出一种使用VTK类库实现三维拾取的方法,能够将用户指定的屏幕坐标转化为三维物体坐标系下的空间坐标.最后通过牙齿模型的三维测量实验,证明这种方法具有良好的交互性和实时性.     

基于三维拼接技术的三维测量系统的开发

提出一种利用参考底座对三维数据进行剪裁拼接的新方法并应用于360°三维全场测量.通过对测量算法、控制软件、云台以及计算机串口通信的系列研究,开发出相应的程序和控制硬件,最终建立了采用面结构光照明的、自动化的360°三维形貌测量系统.该系统给工业三维全场测量提供了一个可操作平台,精确度也达到了实用要求.     

基于平面图像实现三维仿真场景的技术方法研究

利用Windows操作系统平台正的DirectX多媒体接口技术,本文提出了利用平面图像实现三维仿真场景的相关实用技术方法。     

Camera geometries for image matching in 3-D machine vision

The location of a scene element can be determined from the disparity of two of its depicted entities (each in a different image). Prior to establishing disparity, however, the correspondence problem must be solved. It is shown that for the axial-motion stereo camera model the probability of determining unambiguous correspondence assignments is significantly greater than that for other stereo camera models. However, the mere geometry of the stereo camera system does not provide sufficient information for uniquely identifying correct correspondences. Therefore, additional constraints derived from justifiable assumptions about the scene domain and from the scene radiance model are utilized to reduce the number of potential matches. The measure for establishing the correct correspondence is shown to be a function of the geometrical constraints, scene constraints, and scene radiance model     

基于图像识别的建筑物三维重建

为从彩色高分辨率的图像中提取出主要建筑物的位置信息并进行三维重建,提出一种结合2D和3-D信息识别建筑物,通过纹理集技术进行大规模3-D重建的方法.进行边缘检测,提取可用的短直线以及相应的2-D特征,对这些短直线进行逐级聚类得到候选屋顶集合;通过朴素贝叶斯分类器在候选屋顶集合中区分出不同的3D屋顶特征,以识别全局优秀的屋顶;根据屋顶的位置信息,在大规模的3-D场景中绘制出相应尺寸的模型,通过动态分配算法将建筑物纹理合并,减少纹理数量,从而减少纹理状态的切换.以航拍和卫星遥感彩色高分辨率图像进行实验,实验结果表明,该方法有优秀的识别正确率和3-D重建效果.     

基于Kinect深度图像的三维重建

随着机器视觉理论的发展和硬件技术的进步,三维重建在生产、生活中的应用越来越广泛,基于Kinect传感器的三维重建得到广泛的应用。针对于现有的Kinect传感器获得的深度图像深度信息丢失的问题,提出了一种新的基于均值滤波的方法对深度图像进行去噪,并对深度图像进行预处理,获取三维点云,用迭代最近点(Iterative Closest Point,ICP)算法完成点云的精确配准,从而得到配准后物体表面三维点云,并完成物体的三维重建。     

3-D motion estimation in model-based facial image coding

An approach to estimating the motion of the head and facial expressions in model-based facial image coding is presented. An affine nonrigid motion model is set up. The specific knowledge about facial shape and facial expression is formulated in this model in the form of parameters. A direct method of estimating the two-view motion parameters that is based on the affine method is discussed. Based on the reasonable assumption that the 3-D motion of the face is almost smooth in the time domain, several approaches to predicting the motion of the next frame are proposed. Using a 3-D model, the approach is characterized by a feedback loop connecting computer vision and computer graphics. Embedding the synthesis techniques into the analysis phase greatly improves the performance of motion estimation. Simulations with long image sequences of real-world scenes indicate that the method not only greatly reduces computational complexity but also substantially improves estimation accuracy     

Detecting 3-D motion field from range image sequences

An algorithm for computing three-dimensional (3-D) velocity field and motion parameters from a range image sequence is presented. It is based on a new integral 3-D rigid motion invariant feature-the trace of a 3x3 "feature matrix" related to the moment of inertia tensor. This trace can be computed at every point of a moving surface and provides a quantitative measure of the local shape of the surface. Based on the feature's conservation along the trajectory of every moving point, a 3-D Flow Constraint Equation is formulated and solved for the velocity field. The robustness of the feature in presence of noise and discontinuity is analyzed.     

An image processing system for quantitatively analyzing the 2-D fluid velocity-field image

The quantitative analysis of the 2-D fluid velocity field plays an important role in the investigation of fluid mechanics and other areas. In this paper a new system for analyzing the 2-D fluid image is presented. The 2-D flow visualization image is input through a TV camera, and then followed by a set of processes to extract the velocity information, finally the principal parameters of the flow field are computed and displayed. In this system the control strategy is mixed bottom-up and top-down and knowledge of the fluid mechanics is introduced on several levels to improve the performance of the system. For this reason, it is more efficient and has more powerful functions than conventional systems by reducing the human intervention.     

3-D translational motion and structure from binocular image flows

Image flow fields from parallel stereo cameras are analyzed to determine the relative 3-D translational motion of the camera platform with respect to objects in view and to establish stereo correspondence of features in the left and right images. A two-step procedure is suggested. In the first step, translational motion parameters are determined from linear equations the coefficients of which consist of the sums of measured quantities in the two images. Separate equations are developed for cases when measurements of either the full optical flow or the normal flow are available. This computation does not require feature-to-feature correspondence. In addition, no assumption is made about the surfaces being viewed. In the second step of the calculation, with the knowledge of the estimated translational motion parameters, the binocular flow information is used to find features in one image that correspond to given features in the other image. Experimental results with synthetic and laboratory images indicate that the method provides accurate results even in the presence of noise     

3-D shape from a shaded and textural surface image

A new 3D texture model is developed by considering the scene image as the superposition of a random texture image on a smooth shaded image. The whole image is analyzed using a patch-by-patch process. Each patch is assumed to be a tilted and slanted texture plane. A modified reflectance map function is applied to describe the deterministic part, and the fractional differencing periodic model is chosen to describe the random texture because of its good performance in texture synthesis and its ability to represent the coarseness and the pattern of the surface at the same time. An orthographical projection technique is developed to deal with this particular random model, which has a nonisotropically distributed texture pattern. For estimating the parameter, a hybrid method that uses both the least square and the maximum-likelihood estimates is applied directly to the given intensity function. By using these parameters, the synthesized image is obtained and used to reconstruct the original image     

Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the med- ical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics （LARFS） was proposed. By combining segmentation algorithm principles for traditional region growing （RG） and robust feature statistics （RFS）, the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And be- cause the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.     

Techniques for calibration of the scale factor and image center forhigh accuracy 3-D machine vision metrology

Techniques are described for calibrating certain intrinsic camera parameters for machine vision. The parameters to be calibrated are the horizontal scale factor, and the image center. The scale factor calibration uses a one-dimensional fast Fourier transform and is accurate and efficient. It also permits the use of only one coplanar set of calibration points for general camera calibration. Three groups of techniques for center calibration are presented: Group I requires using a laser and a four-degree-of-freedom adjustment of its orientation, but is simplest in concept and is accurate and reproducible; Group II is simple to perform, but is less accurate than the other two; and the most general, Group II, is accurate and efficient, but requires a good calibration plate and accurate image feature extraction of calibration points. Group II is recommended most highly for machine vision applications. Results of experiments are presented and compared with theoretical predictions. Accuracy and reproducibility of the calibrated parameters are reported, as well as the improvement in actual 3-D measurement due to center calibration     

Stratification approach for 3-D euclidean reconstruction of nonrigid objects from uncalibrated image sequences.

This paper addresses the problem of 3-D reconstruction of nonrigid objects from uncalibrated image sequences. Under the assumption of affine camera and that the nonrigid object is composed of a rigid part and a deformation part, we propose a stratification approach to recover the structure of nonrigid objects by first reconstructing the structure in affine space and then upgrading it to the Euclidean space. The novelty and main features of the method lies in several aspects. First, we propose a deformation weight constraint to the problem and prove the invariability between the recovered structure and shape bases under this constraint. The constraint was not observed by previous studies. Second, we propose a constrained power factorization algorithm to recover the deformation structure in affine space. The algorithm overcomes some limitations of a previous singular-value-decomposition-based method. It can even work with missing data in the tracking matrix. Third, we propose to separate the rigid features from the deformation ones in 3-D affine space, which makes the detection more accurate and robust. The stratification matrix is estimated from the rigid features, which may relax the influence of large tracking errors in the deformation part. Extensive experiments on synthetic data and real sequences validate the proposed method and show improvements over existing solutions.