基于直线光流场的三维运动和结构重建

利用直线间运动对应关系，将像素点光流的概念和定义方法应用于直线，提出了直线光流的概念，建立了求解空间物体运动参数的线性方程组，利用三幅图像21条直线的光流场，可以求得物体运动的12个参数以及空间直线坐标．但是在实际应用当中，要找出这21条直线的光流场是很困难的，因此该文提出了运用解非线性方程组的方法，只需要6条直线的光流．就可以分步求出物体的12个运动参数，并根据求得的12个运动参数和一致的图像坐标系中的直线坐标，求得空间直线的坐标，从而实现了三维场景的重建．     

点线光流预测机制的图像序列运动直线跟踪

目的 针对复杂场景图像序列中运动直线特征的提取、跟踪问题,提出一种基于点、线光流预测机制的图像序列运动直线跟踪方法。方法 首先根据图像直线的表达式定义点、线光流基本约束方程,由基本约束方程推导出关于点光流与直线光流对应关系的3个重要推论。然后依据点、线光流对应关系,利用图像序列中直线特征上的像素点光流计算直线光流的估计值并根据直线光流阈值筛选图像序列运动直线。最后由筛选出的运动直线及直线光流估计值计算直线的预测坐标并在Hough域内进行跟踪匹配,得到图像序列运动直线跟踪结果。结果 通过合成及真实图像序列实验验证,本文方法能够准确地筛选出图像序列中感兴趣的运动直线,并对运动直线进行稳定地跟踪、匹配,直线跟踪结果未产生干扰直线的误匹配,直线跟踪时间消耗不超过12 s。结论 相对于传统的直线跟踪、匹配方法,本文方法具有较高地直线跟踪精度和较好的鲁棒性,更适用于复杂场景下的运动直线跟踪、匹配问题。     

基于Murkowski距离连续的非刚体三维运动恢复算法研究

现有的非刚体三维运动重建算法很难根据不同的场景、不同的非刚体来寻找不同的形状基,这种情况对重建 过程产生了很大的影响,造成模型失真。为了解决这一问题,提出一种基于Murkowski距离连续的非刚体三维运动 恢复算法,该算法根据在高速分解的图像序列中帧与帧之间的运动参数与特征点位移变化都呈现连续平缓的物理特 性,在Murkowski距离约束的情况下,将运动结构参数通过非线性优化的方法来进行求解,最终得到非刚体的三维运 动结构,并且通过仿真实验,验证了它的可行。     

基于三参数模型的快速全局运动估计

提出了一种新的全局运动估计方法——基于三参数模型的快速全局运动估计.新的参数模型在保证准确性的同时,使用更少的参数来描述和估计全局运动,从而简化了计算复杂度.此外,对光流场计算做出了两方面改进：（1）提出了宏块预判的方法,计算光流场前对宏块的梯度信息进行预分析,通过减少参与计算的宏块数目提高光流场的计算速度;（2）提出了快速估计宏块运动向量的方法,在块匹配的过程中同时考虑图像的梯度和灰度信息,通过引入更多的约束提高运动向量的计算速度.实验证明了该方法的有效性.     

基于一维移动物体的双目装置自标定

给出了平移运动的一维物体所在平面的虚圆点图像及其对摄像机内参数的约束,和约束方程的数值求解方法,从而获得摄像机的内参数. 进一步通过恢复空间点在摄像机坐标系中的坐标,求解出双目摄像机之间的方位,即摄像机的外参数．对于一维物体的一般刚体运动,给出了把它转化为平移运动的方法．模拟实验和真实图像实验结果表明该方法具有较高的求解精度,同时也有一定的应用价值．     

一种具有自适应权值系数的全局平滑光流算法

分析了Horn-Schunk方法在运动边界处,光流场不能很好地保持不连续性的原因,并从3个方面对Horn-Schunk迭代模型作了改进：（1）在能量方程中用可变的权值系数代替原来的常权值系数;（2）采用一种新方法求解迭代方程中的速度均值,新方法体现了邻域的亮度差别对速度扩散的影响;（3）引入补偿迭代方法去求解相关Euler-Lagrange方程,实验证明这种迭代方法比Gauss-Seidel方法更加有效。试验结果验证了改进的光流求解模型提高了光流场的计算精度,并能更好地保持光流场在运动边界处的不连续性。     

基于射影不变量的摄像机自标定方法

为了方便而精确地进行摄像机标定,提出了一种基于射影不变量的求解摄像机内参数的方法,该方法利用交比在射影变换中不变的性质,通过同一平面中相交直线的无穷远点与虚圆点的交比,先求出同一模板的不同方位的3幅图像中的虚圆点的像;然后利用绝对二次曲线的像在摄像机做刚体运动时保持不变的性质,进而求出绝对二次曲线的像;最后对结果进行Cholesky分解,就可以得到摄像机的内参数。实验表明,该方法行之有效,可以达到较高的精度。     

运动员三维运动图像姿态参数测量的仿真分析

研究运动员运动中的三维图像姿态参数准确测量问题.运动过程中,人体的关节运动程度较为复杂,并且运动速度很快,关节间的细微变化角度很难运用固定的约束算法进行约束.传统的三维运动图像建模运用固定约束模型,很难对上述动态小区域变化进行描述,导致对细微运动姿态的测量效果不好.提出一种运动员三维运动姿态测量方法,通过对人体运动参数进行优化,把运动参数的约束优化问题看成一个非线性求解最小化的问题,运用L-M运动约束参数提供快速收敛的正则化方法,求取非刚体三维运动时的运动参数和结构参数矩阵,完成运动三维参数测量.仿真结果表明,上述方法可以高精度测量运动员的三维运动参数,提高参数测量精度.     

运用视频技术的快速三维旋转分析与计算的研究

着重讨论了基于光流场的旋转运动矢量的估计方法,它是一种非接触式的运动测量技术,对特殊的场合具有 很重要的应用价值。研究中运用的是光流场特征法,即首先建立刚体运动方程,然后根据特征点对坐标,采用两步迭 代交替法计算出运动方程参数,进而计算出测量对象的旋转矢量。为了提高运算的速度,对投影平面上的位移矢量测 量采用了基于灰度编码的位平面的块匹配算法,该算法中以简单的逻辑异或运算来完成两帧中的特征块匹配搜索,以 降低运算复杂度。最后通过一组实验验证了测量结果是比较准确的。     

运动细节估计的光流场方法

针对现有的光流方法在处理大位移和估计运动图像细节方面存在的问题,提出一种结合图像细节特征的变分光流场模型.首先通过增加特征点的对应,采用自适应的保持边缘的正则项以及引入occlusion检测函数对经典光流模型进行了改进;其次,采用基于变分框架下的高斯金字塔方法以及加权中值滤波的方法对所提出的模型进行求解.大量的实验结果...     

Unified optical flow field approach to motion analysis from a sequence of stereo images

In this paper a new approach to motion analysis from stereo image sequences using unified temporal and spatial optical flow field (UOFF) is reported. That is, based on a four-frame rectangular model and the associated six UOFF field quantities, a set of equations is derived from which both position and velocity can be determined. It does not require feature extraction and correspondence establishment, which are known to be difficult, and only partial solutions suitable for simplistic situations have been developed. Furthermore, it is capable of detecting multiple moving objects even when partial occlusion occurs, and is potentially suitable for nonrigid motion analysis. Unlike the current existing techniques for motion analysis from stereo imagery, the recovered motion by using this new approach is for a whole continuous field instead of only for some features. It is a purely optical flow approach. Two experiments are presented to demonstrate the feasibility of the approach.     

Qualitative estimation of camera motion parameters from the linear composition of optical flow

In this paper, we propose a new method for estimating camera motion parameters based on optical flow models. Camera motion parameters are generated using linear combinations of optical flow models. The proposed method first creates these optical flow models, and then linear decompositions are performed on the input optical flows calculated from adjacent images in the video sequence, which are used to estimate the coefficients of each optical flow model. These coefficients are then applied to the parameters used to create each optical flow model, and the camera motion parameters implied in the adjacent images can be estimated through a linear composition of the weighted parameters.We demonstrated that the proposed method estimates the camera motion parameters accurately and at a low computational cost as well as robust to noise residing in the video sequence being analyzed.     

Multi-Exposure Motion Estimation Based on Deep Convolutional Networks

In motion estimation, illumination change is always a troublesome obstacle, which often causes severely performance reduction of optical flow computation. The essential reason is that most of estimation methods fail to formalize a unified definition in color or gradient domain for diverse environmental changes. In this paper, we propose a new solution based on deep convolutional networks to solve the key issue. Our idea is to train deep convolutional networks to represent the complex motion features under illumination change, and further predict the final optical flow fields. To this end, we construct a training dataset of multi-exposure image pairs by performing a series of non-linear adjustments in the traditional datasets of optical flow estimation. Our multi-exposure flow networks (MEFNet) model consists of three main components: low-level feature network, fusion feature network, and motion estimation network. The former two components belong to the contracting part of our model in order to extract and represent the multi-exposure motion features; the third component is the expanding part of our model in order to learn and predict the high-quality optical flow. Compared with many state-of-the-art methods, our motion estimation method can eliminate the obstacle of illumination change and yield optical flow results with competitive accuracy and time efficiency. Moreover, the good performance of our model is also demonstrated in some multi-exposure video applications, like HDR (high dynamic range) composition and flicker removal.     

基于运动补偿和MCMC算法的视频目标跟踪

针对视频目标跟踪领域摄像头运动等问题,提出一种基于二次观测模型的马尔科夫链蒙特卡洛(MCMC)粒子滤波算法。第1次观测通过计算相邻2帧的光流场对运动模型实时修正使其逼近真实的运动方程,第2次观测MCMC粒子滤波步骤。二次观测模型利用图像中的光流信息进行运动补偿实现跟踪。时变的运动模型可以有效提高MCMC方法的效率,减少无效的粒子点数,使其能更快速地收敛到真实值。实验表明对MCMC进行运动补偿可以有效处理摄像头运动问题。     

On kineopsis and computation of structure and motion

This correspondence deals with the computation of structure and motion of rigid objects in space from image positions and optical flow. A test for rigid motion of objects in space is introduced which yields a new formulation of the problem. Assuming a central projection model for the viewing system, it is shown that image positions and optical flow at four points can achieve this perception.     

A General Motion Model and Spatio-Temporal Filters for Computing Optical Flow

Traditional optical flow algorithms assume local image translational motion and apply simple image filtering techniques. Recent studies have taken two separate approaches toward improving the accuracy of computed flow: the application of spatio-temporal filtering schemes and the use of advanced motion models such as the affine model. Each has achieved some improvement over traditional algorithms in specialized situations but the computation of accurate optical flow for general motion has been elusive. In this paper, we exploit the interdependency between these two approaches and propose a unified approach. The general motion model we adopt characterizes arbitrary 3-D steady motion. Under perspective projection, we derive an image motion equation that describes the spatio-temporal relation of gray-scale intensity in an image sequence, thus making the utilization of 3-D filtering possible. However, to accommodate this motion model, we need to extend the filter design to derive additional motion constraint equations. Using Hermite polynomials, we design differentiation filters, whose orthogonality and Gaussian derivative properties insure numerical stability; a recursive relation facilitates application of the general nonlinear motion model while separability promotes efficiency. The resulting algorithm produces accurate optical flow and other useful motion parameters. It is evaluated quantitatively using the scheme established by Barron et al. (1994) and qualitatively with real images.     

Model-Based Multiresolution Motion Estimation in Noisy Images

It is argued that accurate optical flow can only be determined if problems such as local motion ambiguity, motion segmentation, and occlusion detection are simultaneously addressed. To meet this requirement, a new multiresolution region-growing algorithm is proposed. This algorithm consists of a region-growing process which is able to segment the flow field in an image into homogeneous regions which are consistent with a linear affine flow model. To ensure stability and robustness in the presence of noise, this region-growing process is implemented within the hierarchical framework of a spatial lowpass pyramid. The results of applying this algorithm to both natural and synthetic image sequences are presented.     

Adjusting shape parameters using model-based optical flow residuals

We present a method for estimating the shape of a deformable model using the least-squares residuals from a model-based optical flow computation. This method is built on top of an estimation framework using optical flow and image features, where optical flow affects only the motion parameters of the model. Using the results of this computation, our new method adjusts all of the parameters so that the residuals from the flow computation are minimized. We present face tracking experiments that demonstrate that this method obtains a better estimate of shape compared to related frameworks     

On the Spatial Statistics of Optical Flow

We present an analysis of the spatial and temporal statistics of “natural” optical flow fields and a novel flow algorithm that exploits their spatial statistics. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from hand-held and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a Field-of-Experts model that captures the spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.