基于线性正则变换的复杂运动目标的3-D InISAR成像

二维的逆合成孔径雷达(two-dimensional inverse synthetic aperture radar, 2-D ISAR)成像是目标三维(three-dimensional, 3-D)几何结构在成像平面的投影,逐渐难以满足现代工程中目标识别与分类的需求,特别是复杂运动目标.近年来,干涉ISAR (interferometric ISAR, InISAR)成像技术能够高分辨地重构3-D目标的几何结构而颇受关注.本文提出一种基于线性正则变换(linear canonical transform, LCT)的复杂运动目标的3-D InISAR成像算法.首先,结合目标复杂运动分析,给出了目标所产生的原始回波信号.经过运动补偿和图像对准后,将方位向回波建模为线性调频(linear frequency modulation, LFM)信号.其次,根据LFM信号的时频特性,本文提出了基于LCT的2-D ISAR成像算法.该算法不仅能够获取聚焦的多通道2-D ISAR图像,而且很好地保留了相位信息.最后,利用干涉技术,提出了新的3-D InISAR成像算法,实现了目标的高分辨3-D几...     

信号博弈的三维重建形变识别算法

现有双目立体视觉算法常常需要双目相机位置固定,在现实应用中,这类算法难以重构空间三维几何关系.为此,本文提出了一种不受位置限制的多视角图像三维重建与形变检测算法.该算法首先采用sift算法获取成对图像的特征点,以获取形变前后比对点对图像的特征信息;其次,利用信号博弈方法确定图像拍摄时相机的空间位置与视角,以准确获取图像的空间位置坐标;再次,依据上述信息完成物体的三维点云重建;进而,利用三维数据信息比对实现物体形变识别.最后,本文利用真实物体的实验,验证了三维重建形变识别算法的有效性.     

Unity3D虚拟现实技术在三维图像场景重构中的应用

现今使用的图像场景重构方法主要利用模型跟踪图像特征细节,通过空间转换得到重构后的场景。由于缺乏对场景深度的分析,导致重构后的场景误差较大。提出利用Unity3D虚拟现实（Virtual Reality,VR）技术重构三维图像场景。首先,根据双目成像原理获取场景深度,滤波处理图像中的点云数据。其次,引入指数权重系数描述图像特征,将详细的场景信息输入Unity3D。最后,借助Unity3D处理组件实现场景重构。经实验表明,利用Unity3D重构的结构相似性平均为93.94%,场景体素并交比数值较高,重构质量良好。     

基于SURF特征点的自主空中加油定位研究

针对硬式空中加油技术中加油插座的定位问题,提出了一种结合SURF(Speeded-up Robust Features)特征点的双目立体视觉定位方法,确定了加油插座的空间位置,实现伸缩管和加油插座的精确对准。基于SURF匹配算法,对双目视觉系统采集的左右图像进行目标检测,并获取匹配目标的SURF特征点,通过空间误匹配点对的剔除和目标点坐标的计算,确定左右图像中具有空间位置一致性的目标点(即加油插座),从而恢复目标点的三维信息。实验结果表明,结合SURF方法能够有效地对加油插座的空间位置进行定位,具有一定的实用价值。     

光学动作捕捉中的摄像机标定

摄像机标定是光学动作捕捉中必不可少的部分,其作用是将摄像机获取的2D点还原为空间3D点.其主要工作是若干个摄像机对带有跟踪点的物体进行拍摄,根据空间中3D坐标与摄像机图像中2D坐标的对应关系,建立标定方程,对标定方程作一定的变换,得到计算相对简单快速的线性方程,最终求得用来将2D点还原成3D点的六个摄像机内部参数和外部参数(旋转矩阵和位移坐标).     

基于粒子滤波的双模图像融合自适应跟踪

针对视频跟踪中仅利用目标的单特征容易导致跟踪失败的问题,提出一种基于粒子滤波的可见光与红外序列图像相融合的自适应目标跟踪算法;该算法在粒子滤波跟踪算法框架下,根据单一信源运动目标序列图像的品质因子,利用自适应加权融合策略重构双模序列图像的特征选择机制,建立了基于自适应融合算法的系统观测概率模型和状态空间层次采样多特征融合跟踪算法,实现了对双模序列图像的融合以及对运动目标的稳健跟踪;跟踪试验结果表明,该算法可以有效实现对运动目标的稳健、准确跟踪。     

基于双目立体视觉的机器人定位系统设计与实现

针对传统工业机器人预先示教或单目视觉引导应用的局限性,提出一种基于双目立体视觉的机器人定位系统设计,获取目标物体在空间中的三维信息。该系统设计基于ABB机器人执行系统,使用TCP触碰标定板的方式完成双相机的标定,采用SIFT斑点算法获取工件的特征,结合Opencv3.4.1库,基于图像特征点对左右相机获得的图像进行立体匹配,得到物体的三维点坐标,引导机器人实现抓取。     

基于正交立体图像的3D人体姿态重建算法

提出一种由目标的立体图像通过人工神经网络实时估计得到其3D姿态的方法。网络的输入向量由同步立体图像帧上目标特征点的坐标构成;而输出向量则表示目标若干关键位置的三维姿态（进而可以建立目标的3D模型）。拟合该神经网络所需要的输出样本数据由运动捕获系统REACTOR获取。实验表明基于该算法的3D姿态估计误差低于5%,可以有效应用于3D虚拟目标的计算机实时合成等。     

基于SLM显微立体视觉模型的3D微定位研究

基于体视显微镜(Stereolightmicroscope,SLM)的显微立体视觉已经在微操作领域应用。本文研究了基于SLM显微立体视觉模型的微操作系统中的三维微观定位问题。通过对SLM双光路的分析,给出了描述二维图像空间和三维物空间映射的弱非线性显微立体视觉模型。采用立体匹配算法和目标识别两种方式对运动图像序列中的目标对象进行捕捉,可以批量给出立体图像中相关点的坐标。利用显微立体视觉理论模型和显微图像处理实现了微操作系统中的微观3D定位。     

基于胶囊内窥镜图像的肠胃道三维重建技术

为了改进胶囊内窥镜观测的准确性和真实性,提出了基于胶囊内窥镜序列图像的胃肠道三维重建的方法.首先利用SIFT算法提取前后两幅序列图像中尽可能多的对应特征点;计算获取各特征点在成像面上的二维坐标;进一步利用8点算法计算胶囊内镜运动变化的旋转矩阵和平移矢量.进而计算得到每个特征点的相对三维坐标和世界三维坐标;然后,采用Delaunay三角剖分算法对各三维点进行网格化,并完成场景的三维重建.实验表明相机与被测点距离在100 mm之内时,得到的深度误差小于1 mm;距离250 mm内时,相对误差在3％之内.说明所提出的算法是可行的.     

Part I: Modeling image curves using invariant 3-D object curvemodels-a path to 3-D recognition and shape estimation from imagecontours

This paper and its companion are concerned with the problems of 3-D object recognition and shape estimation from image curves using a 3-D object curve model that is invariant to affine transformation onto the image space, and a binocular stereo imaging system. The objects of interest here are the ones that have markings (e.g., characters, letters, special drawings and symbols, etc.) on their surfaces. The 3-D curves on the object are modeled as B-splines, which are characterized by a set of parameters (the control points) from which the 3-D curve can be totally generated. The B-splines are invariant under affine transformations. That means that the affine projected object curve onto the image space is a B-spline whose control points are related to the object control points through the affine transformation. Part I deals with issues relating to the curve modeling process. In particular, the authors address the problems of estimating the control points from the data curve, and of deciding on the “best” order B-spline and the “best” number of control points to be used to model the image or object curve(s). A minimum mean-square error (mmse) estimation technique which is invariant to affine transformations is presented as a noniterative, simple, and fast approach for control point estimation. The “best” B-spline is decided upon using a Bayesian selection rule. Finally, we present a matching algorithm that allocates a sample curve to one of p prototype curves when the sample curve is an a priori unknown affine transformation of one of the prototype curves stored in the data base. The approach is tried on a variety of images of real objects     

基于时频分布级数的时频ISAR成像

传统的逆合成孔径雷达(ISAR)成像算法用傅立叶变换进行频谱分析,对机动目标的成像会导致成像模糊.应用了时频分布级数方法代替传统的傅立叶变换对机动目标进行瞬时成像,得到一个三维的时间-距离-多普勒阵,对时间进行采样即得到瞬时的二维距离-多普勒像.它不仅具有很好的分辨率,还具有瞬时信息,并克服了短时傅立叶变换低的时频集聚性和魏格纳分布交叉项严重的缺点.仿真结果表明,通过该方法能得到清晰的目标的距离-瞬时多普勒像.     

Three-dimensional image mosaicking using multiple projection planes for 3-D visualization of roadside standing buildings.

A novel image-mosaicking technique suitable for 3-D visualization of roadside buildings on websites or mobile systems is proposed. Our method was tested on a roadside building scene taken using a side-looking video camera employing a continuous set of vertical-textured planar faces. A vertical plane approximation of the scene geometry for each frame was calculated using sparsely distributed feature points that were assigned 3-D data through bundle adjustments. These vertical planes were concatenated to create an approximate model on which the images could be backprojected as textures and blended together. Additionally, our proposed method includes an expanded crossed-slits projection around far-range areas to reduce the "ghost effect," a phenomenon in which a particular object appears repeatedly in a created image mosaic. The final step was to produce seamless image mosaics using Dijkstra's algorithm to find the optimum seam line to blend overlapping images. We used our algorithm to create efficient image mosaics in 3-D space from a sequence of real images.     

3-d motion estimation, understanding, and prediction from noisy image sequences

This paper presents an approach to understanding general 3-D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Specifically, the model constrains the motion, over a local frame subsequence, to be a superposition of precession and translation. Thus, the instantaneous rotation axis of the object is allowed to change through the subsequence. The trajectory of the rotation center is approximated by a vector polynomial. The parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics. The nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence. The estimation algorithm presented in this paper is linear, i.e., the algorithm consists of solving simultaneous linear equations. Based on the assumption that the motion is smooth, object positions and motion in the near future can be predicted, and short missing subsequences can be recovered. Noise smoothing is achieved by overdetermination and a leastsquares criterion. The framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames.     

基于径向基函数与B样条曲线的三维人脸重建方法

用二维人脸照片构造三维特定人脸模型是三维人脸建模研究的热点问题.采用立体视觉重建三维特征点,通过人脸正侧面照片特征点的分层、分部位的径向基函数重构一般人脸模型,得到部位轮廓特征明显的三维特定人脸模型,利用B样条曲线进行三维人脸曲面重构;然后运用拉普拉斯金字塔法得到近似的人脸全视角纹理图;再将近似人脸全视角纹理图映射到三维特定人脸模型,从而得到三维特定真实感的人脸模型.实验结果表明,该方法计算简单,生成特定人脸模型效果逼真,可用于特定人脸动画制作的逼真人脸建模.     

结合边缘与灰度信息的SAR图像配准算法

同一场景下的合成孔径雷达(SAR)图像的灰度特性由于相关噪声的影响及成像条件不同可能存在很大差异,使得单纯基于边缘特征或灰度信息的方法难以胜任SAR图像配准工作。根据SAR图像的特点,提出一种典型地物边缘形状信息与局部灰度统计信息相结合的基于特征的图像配准方法,弥补了仅利用边缘特征或灰度信息的方法在SAR图像配准中的不足。给出了本方法用于Radarsat图像上的实验结果。     

Colour segmentation and highlight detection on a 1-D feature space

Specularly reflecting surfaces often corrupt a real scene and degrade system performance. In this paper, we present an algorithm for colour segmentation as well as highlight detection. This algorithm models the human colour vision perception with the physical properties of sensors, illumination and surface reflectances. For image to be dependent only on the body reflectance, the reflection due to illumination, shading and highlights should be discounted. The dichromatic reflection model which describes the colour of the reflected light as a linear combination of the colour of the light due to surface reflection (highlights) and body reflection (object colour) is used. The input image data is first mapped from device coordinates onto the CIE 1931 xy chromaticity diagram where colour clustering occurs. Since the feature space is 3-D, colour clustering is a computationally expensive process. A more efficient method is the dimensionality reduction of the feature space. Using information from the xy chromaticity diagram, the estimated colour clusters are then projected onto a line for 1-D thresholding. The error of projection is minimized by the Fisher's linear discriminant method. A program recognizing nine colours on a 1024 × 1024 image is presented. Colour segmentation of 99% accuracy within 25 s is also achieved.     

3-D motion estimation using a sequence of noisy stereo images:models, estimation, and uniqueness results

A kinematic model-based approach for the estimation of 3-D motion parameters from a sequence of noisy stereo images is discussed. The approach is based on representing the constant acceleration translational motion and constant precession rotational motion in the form of a bilinear state-space model using standard rectilinear states for translation and quaternions for rotation. Closed-form solutions of the state transition equations are obtained to propagate the quaternions. The measurements are noisy perturbations of 3-D feature points represented in an inertial coordinate system. It is assumed that the 3-D feature points are extracted from the stereo images and matched over the frames. Owing to the nonlinearity in the state model, nonlinear filters are designed for the estimation of motion parameters. Simulation results are included. The Cramer-Rao performance bounds for motion parameter estimates are computed. A constructive proof for the uniqueness of motion parameters is given. It is shown that with uniform sampling in time, three noncollinear feature points in five consecutive binocular image pairs contain all the spatial and temporal information. Both nondegenerate and degenerate motions are analyzed. A deterministic algorithm to recover motion parameters from a stereo image sequence is summarized from the constructive proof     

Matching 3-D Models to 2-D Images

We consider the problem of analytically characterizing the set of all 2-D images that a group of 3-D features may produce, and demonstrate that this is a useful thing to do. Our results apply for simple point features and point features with associated orientation vectors when we model projection as a 3-D to 2-D affine transformation. We show how to represent the set of images that a group of 3-D points produces with two lines (1-D subspaces), one in each of two orthogonal, high-dimensional spaces, where a single image group corresponds to one point in each space. The images of groups of oriented point features can be represented by a 2-D hyperbolic surface in a single high-dimensional space. The problem of matching an image to models is essentially reduced to the problem of matching a point to simple geometric structures. Moreover, we show that these are the simplest and lowest dimensional representations possible for these cases.We demonstrate the value of this way of approaching matching by applying our results to a variety of vision problems. In particular, we use this result to build a space-efficient indexing system that performs 3-D to 2-D matching by table lookup. This system is analytically built and accessed, accounts for the effects of sensing error, and is tested on real images. We also derive new results concerning the existence of invariants and non-accidental properties in this domain. Finally, we show that oriented points present unexpected difficulties: indexing requires fundamentally more space with oriented than with simple points, we must use more images in a motion sequence to determine the affine structure of oriented points, and the linear combinations result does not hold for oriented points.     

空间轨道目标ISAR成像方法

研究了高速空间目标逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)成像问题;根据空间目标回波模型提出了先进行速度补偿再进行平动补偿的ISAR成像方法。研究了高速运动目标回波模型,针对空间目标回波为线性调频信号的特点,提出采用CLEAN算法的线性调频信号参数估计方法对回波进行速度补偿。最后对自旋和非自旋两类轨道飞行目标成像进行了分析。仿真结果验证了理论分析的正确性和成像方法的有效性。