基于雷达和图像融合的3D车辆定位与识别

本文针对三维车辆的定位和识别问题,提出了一种融合雷达和图像信息的新方法.结合雷达的滤波信息和图像的灰度信息建立视觉窗口,确定车辆的平移参数,并缩小了图像处理区域,降低了环境噪声.以改进的Hausdorff距离为依据建立目标的姿态评价函数,通过全局寻优确定车辆的旋转参数,降低了计算损耗,避免了噪声点对模型匹配的影响.车辆识别以定位技术为基础,各模型在最优姿态下的评价函数值决定了车辆的类型.三维仿真场景实验证明,该方法能有效地实现车辆的定位与识别.     

Multicamera Tracking of Articulated Human Motion Using Shape and Motion Cues

We present a completely automatic algorithm for initializing and tracking the articulated motion of humans using image sequences obtained from multiple cameras. A detailed articulated human body model composed of sixteen rigid segments that allows both translation and rotation at joints is used. Voxel data of the subject obtained from the images is segmented into the different articulated chains using Laplacian Eigenmaps. The segmented chains are registered in a subset of the frames using a single-frame registration technique and subsequently used to initialize the pose in the sequence. A temporal registration method is proposed to identify the partially segmented or unregistered articulated chains in the remaining frames in the sequence. The proposed tracker uses motion cues such as pixel displacement as well as 2-D and 3-D shape cues such as silhouettes, motion residue, and skeleton curves. The tracking algorithm consists of a predictor that uses motion cues and a corrector that uses shape cues. The use of complementary cues in the tracking alleviates the twin problems of drift and convergence to local minima. The use of multiple cameras also allows us to deal with the problems due to self-occlusion and kinematic singularity. We present tracking results on sequences with different kinds of motion to illustrate the effectiveness of our approach. The pose of the subject is correctly tracked for the duration of the sequence as can be verified by inspection.      

An appearance model constructed on 3-D surface for robust face recognition against pose and illumination variations

We propose a face recognition method that is robust against image variations due to arbitrary lighting and a large extent of pose variations, ranging from frontal to profile views. Existing appearance models defined on image planes are not applicable for such pose variations that cause occlusions and changes of silhouette. In contrast, our method constructs an appearance model of a three-dimensional (3-D) object on its surface. Our proposed model consists of a 3-D shape and geodesic illumination bases (GIBs). GIBs can describe the irradiances of an object's surface under any illumination and generate illumination subspace that can describe illumination variations of an image in an arbitrary pose. Our appearance model is automatically aligned to the target image by pose optimization based on a rough pose, and the residual error of this model fitting is used as the recognition score. We tested the recognition performance of our method with an extensive database that includes 14 000 images of 200 individuals with drastic illumination changes and pose variations up to 60/spl deg/ sideward and 45/spl deg/ upward. The method achieved a first-choice success ratio of 94.2% without knowing precise poses a priori.     

Tracking a dynamic set of feature points

We address the problems of tracking a set of feature points over a long sequence of monocular images as well as how to include and track new feature points detected in successive frames. Due to the 3-D movement of the camera, different parts of the images exhibit different image motion. Tracking discrete features can therefore be decomposed into several independent and local problems. Accordingly, we propose a localized feature tracking algorithm. The trajectory of each feature point is described by a 2-D kinematic model. Then to track a feature point, an interframe motion estimation scheme is designed to obtain the estimates of interframe motion parameters. Subsequently, using the estimates of motion parameters, corresponding points are identified to subpixel accuracy. Afterwards, the temporal information is processed to facilitate the tracking scheme. Since different feature points are tracked independently, the algorithm is able to handle the image motion arising from general 3-D camera movements. On the other hand, in addition to tracking feature points detected at the beginning, an efficient way to dynamically include new points extracted in subsequent frames is devised so that the information in a sequence is preserved. Experimental results for several image sequences are also reported.     

基于多通道联合自聚焦技术的机载三维SAR运动补偿

机载3维SAR通过天线阵实现3维的分辨,这也导致在分析3维SAR系统的运动误差时,不但要考虑载机的平动误差带来的天线阵整体平移,更要考虑载机转动误差带来的阵元间相对位置变化,因此针对3维SAR的运动误差的分析与传统单通道SAR相比难度更大。该文从平动误差和转动误差导致的斜距变化出发,利用天线阵的线性几何构型,提出基于多通道联合自聚焦技术的运动补偿算法。该算法可同时得到平动和转动误差,从而使载机运动误差得到精确补偿。最后通过仿真实验验证了算法的有效性。     

一种基于投影的小波矩及其在图像识别中的应用

该文提出了一种基于图像投影的小波不变量算法。首先将图像投影到一维空间,然后引入一种平移和比例不变自适应一维小波变换,利用该变换获得图像的平移和比例不变特征。由于图像的旋转导致特征矩阵发生行之间的循环平移,该文利用奇异值分解进行研究,得到的奇异值向量具有图像的3个不变性。实验分析表明,本文方法具有较好的图像识别效果。     

随机工件的点云场景CAD模型的快速识别与定位

工业机器人精准抓取工件的前提是能够获取到目标工件的位置与位姿的信息。提出一种在三维点云场景对随机摆放目标工件的快速、精准的识别与定位的方法。在复杂点云场景中随机采样并提取关键点组成随机点对表征局部特征,再与由物体CAD模型建立的三维匹配模板进行配准,获得一系列候选位姿。利用表面相似点占比多次优化位姿,确定目标工件最佳位姿。最后利用虚拟相机投影二维视图到图像坐标系直观呈现结果。选择汽车座椅连接件作为验证实验的实验对象,实验结果表明:目标工件平移方向上拟合误差可以达到0.3 mm左右,旋转的拟合误差可以达到0.8°左右,具有较高的稳定与高效性能。     

In vivo measurement of 3-D skeletal kinematics from sequences ofbiplane radiographs: Application to knee kinematics

Current noninvasive or minimally invasive methods for evaluating in vivo knee kinematics are inadequate for accurate determination of dynamic joint function due to limited accuracy and/or insufficient sampling rates. A three-dimensional (3-D) model-based method is presented to estimate skeletal motion of the knee from high-speed sequences of biplane radiographs. The method implicitly assumes that geometrical features cannot be detected reliably and an exact segmentation of bone edges is not always feasible. An existing biplane radiograph system was simulated as two separate single-plane radiograph systems. Position and orientation of the underlying bone was determined for each single-plane view by generating projections through a 3-D volumetric model (from computed tomography), and producing an image (digitally reconstructed radiograph) similar (based on texture information and rough edges of bone) to the two-dimensional radiographs. The absolute 3-D pose was determined using known imaging geometry of the biplane radiograph system and a 3-D line intersection method. Results were compared to data of known accuracy, obtained from a previously established bone-implanted marker method. Difference of controlled in vitro tests was on the order of 0.5 mm for translation and 1.4 degrees for rotation. A biplane radiograph sequence of a canine hindlimb during treadmill walking was used for in vivo testing, with differences on the order of 0.8 mm for translation and 2.5 degrees for rotation.     

基于光流法的多分辨率电子稳像算法

针对动载体摄像系统中视频序列受载体姿态运动及抖动的干扰而出现的不稳定现象,提出一种基于光流算法的多分辨率电子稳像算法。首先,通过划定有效的运动估算区域取代对整帧图像的计算以降低计算量;然后,利用基于光流算法的多分辨率分层运动估计快速并精确地计算出包含平移、旋转以及缩放运动的相邻帧间仿射变换参数;最后,采用固定帧补偿算法,利用求得的仿射变换参数,对图像进行运动补偿,消除或减轻图像序列帧间的随机抖动,达到稳像的目的。实验结果表明,针对包含运动目标的动态场景,该算法可以精确地检测出视频序列帧间平移、旋转以及缩放等复杂的抖动,水平和垂直方向的稳像精确度小于1pixel,保证视频序列的稳定输出,可应用于目标跟踪系统中。     

Traffic accident prediction using 3-D model-based vehicle tracking

Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.     

红外多目标图像生成技术研究

红外多目标图像的仿真,是在对多目标运动关系、红外景物辐射、大气传输、目标红外特性、目标三维特性及噪声仿真深入研究的基础上,抽象出简化的图像生成模型;包括运动模型化、目标模型化、几何变换、红外仿真、扫描填充和噪声仿真几部分。最后将整个研究工作放在微机图像处理环境中,应用实际目标的红外特性,建立了多个运动目标的能够满足实时性要求的红外图像生成系统。     

基于EPNP算法的单目视觉测量系统研究

利用计算机视觉进行姿态测量的方法已广泛应用于现代控制、导航、跟踪等多个领域中。研究并设计了一种基于P4P矩形分布的平面靶标和EPNP算法结合的单目视觉姿态测量方法。首先,利用单相机获取平面靶标图像,经图像处理后得到四个特征点的像素坐标,并使用EPNP算法进行姿态解算;其次,对姿态角测量误差进行了仿真分析,为提高姿态测量精度提供了理论指导和依据;最后,提出一种与高精度二维转台结合的坐标系配准方法,利用该方法对三个方向姿态角精度进行验证。实验结果表明:当绕x和y轴的转动角度在[-6,6]时,姿态测量误差小于0.1,可以满足测量应用需求。     

Three-dimensional surface reconstruction using optical flow formedical imaging

The recovery of a three-dimensional (3-D) model from a sequence of two-dimensional (2-D) images is very useful in medical image analysis. Image sequences obtained from the relative motion between the object and the camera or the scanner contain more 3-D information than a single image. Methods to visualize the computed tomograms can be divided into two approaches: the surface rendering approach and the volume rendering approach. In this paper, a new surface rendering method using optical flow is proposed. Optical flow is the apparent motion in the image plane produced by the projection of real 3-D motion onto the 2-D image. The 3-D motion of an object can be recovered from the optical-flow field using additional constraints. By extracting the surface information from 3-D motion, it is possible to obtain an accurate 3-D model of the object. Both synthetic and real image sequences have been used to illustrate the feasibility of the proposed method. The experimental results suggest that the proposed method is suitable for the reconstruction of 3-D models from ultrasound medical images as well as other computed tomograms     

基于大景深三维扫描仪的机器人"手-眼"标定

针对机器人视觉对便携式三维扫描系统的大景深要求,利用Scheimpflug条件对像平面进行偏转,扩大了扫描系统的景深,并采用了一种将系统整个景深范围分段及进行分段校准的方法,提高了大景深三维扫描系统的测量精度;利用半径已知的球体作为参照工具,提出了一种新的机器人视觉“手-眼”关系标定方法,将姿态关系Rs与位置关系Ts解耦,使用线激光与球体的交线拟合圆恢复球心以及扫描球面拟合球心的方法,分别标定了扫描系统与机器人的旋转和平移关系,从而使机器人能够与扫描仪一起完成扫描任务,扫描精度可以达到0．2mm。实验结果表明,该方法具有精度高及稳定性好的特点。     

3-D-Skeleton-Based Head Detection and Tracking Using Range Images

Vision-based 3-D head detection and tracking systems have been studied in several applications like video surveillance, face-detection systems, and occupant posture analysis. In this paper, we present the development of a topology-based framework using a 3-D skeletal model for the robust detection and tracking of a vehicle occupant's head position from low-resolution range image data for a passive safety system. Unlike previous approaches to head detection, the proposed approach explores the topology information of a scene to detect the position of the head. Among the different available topology representations, the Reeb graph technique is chosen and is adapted to low-resolution 3-D range images. Invariance of the graph under rotations is achieved by using a Morse radial distance function. To cope with the particular challenges such as the noise and the large variations in the density of the data, a voxel neighborhood connectivity notion is proposed. A multiple-hypothesis tracker (MHT) with nearest-neighbor data association and Kalman filter prediction is applied on the endpoints of the Reeb graph to select and filter the correct head candidate out of Reeb graph endpoints. A systematic evaluation of the head detection framework is carried out on full-scale experimental 3-D range images and compared with the ground truth. It is shown that the Reeb graph topology algorithm developed herein allows the correct detection of the head of the occupant with only two head candidates as input to the MHT. Results of the experiments demonstrate that the proposed framework is robust under the large variations of the scene. The processing requirements of the proposed approach are discussed. It is shown that the number of operations is rather low and that real-time processing requirements can be met with the proposed method.     

基于非均匀采样的多模型红外三维目标跟踪

针对红外三维目标跟踪过程中目标姿态变化导致跟踪器失效的问题,提出了一种基于非均匀采样的多模型方法.首先用若干个原型视图表征三维目标,将这些原型视图对应的原型形状作为目标的多模型形状表示,并建立了这些原型形状之间的转移概率矩阵.在粒子滤波框架下,以对数极坐标变换下的原型视图中目标的灰度分布特征作为参考目标模型.通过对形状转移概率采样,实现了样本形状的转移与传播.此方法提高了跟踪器对于姿态变化的鲁棒性,同时具有非均匀采样特性的对数极坐标变换可以抑制图像尺度、旋转造成的畸变,并起到压缩周边的计算量的作用.仿真结果表明,这种算法对三维目标有较好的跟踪效果.     

A visual analysis/synthesis feedback loop for accurate face tracking

We propose a novel approach for face tracking, resulting in a visual feedback loop: instead of trying to adapt a more or less realistic artificial face model to an individual, we construct from precise range data a specific texture and wireframe face model, whose realism allows the analysis and synthesis modules to visually cooperate in the image plane, by directly using 2D patterns synthesized by the face model. Unlike other feedback loops found in the literature, we do not explicitly handle the 3D complex geometric data of the face model, to make real-time manipulations possible. Our main contribution is a complete face tracking and pose estimation framework, with few assumptions about the face rigid motion (allowing large rotations out of the image plane), and without marks or makeup on the user's face. Our framework feeds the feature-tracking procedure with synthesized facial patterns, controlled by an extended Kalman filter. Within this framework, we present original and efficient geometric and photometric modelling techniques, and a reformulation of a block-matching algorithm to make it match synthesized patterns with real images, and avoid background areas during the matching. We also offer some numerical evaluations, assessing the validity of our algorithms, and new developments in the context of facial animation. Our face-tracking algorithm may be used to recover the 3D position and orientation of a real face and generate a MPEG-4 animation stream to reproduce the rigid motion of the face with a synthetic face model. It may also serve as a pre-processing step for further facial expression analysis algorithms, since it locates the position of the facial features in the image plane, and gives precise 3D information to take into account the possible coupling between pose and expressions of the analysed facial images.     

基于IEKF视觉运动分析递归算法的研究

本文提出基于图象序列上特征点的图象平面轨迹进行运动参数估计，以提取运动物体的姿态、速度以及外部环境内所感兴趣点的位置参数，由于图象特征点轨迹与待估计的参数有关，因此能够组合长序列图象内的信息并利用ＩＥＫＦ递归地估计未知的运动及结构参数。     

Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images

This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.     

DETERMINING POSE PARAMETERS WITHOUT PRIMITIVE-TO-PRIMITIVE CORRESPONDENCE

The problem of determining the pose of an object in 3-D space is essential in many computer vision applications. In this paper, a model-based approach for solving this problem is proposed. This approach does not require the knowledge of point-to-point correspondences between 3-D points on the model and 2-D points in the observed image. The spatial location of the object is iteratively estimated and updated from the values globally defined over the model image and the observed image.