一种改进的帧间差光流场算法

结合运动目标检测帧差法运算速度快和光流法活动目标检测准确度高的特点,提出一种改进的帧间差光流场计算的运动目标检测算法。在帧差部分采用隔帧差分从而可以检测到帧间位移小于1个像元而多帧累积位移大于1个像元的运动点目标;在光流计算时,引入通用动态图像模型（GDIM）建立新的光流约束条件,克服了亮度变化引起的约束方程不成立问题。算法仅对帧差法后图像中不为零的像素进行光流场计算,提高了目标检测的准确性和检测速度。仿真实验证明了该算法的有效性。     

基于事件相机的连续光流估计

事件相机对场景的亮度变化进行成像,输出异步事件流,具有极低的延时,受运动模糊问题影响较少.因此,可以利用事件相机解决高速运动场景下的光流(Optical flow, OF)估计问题.基于亮度恒定假设和事件产生模型,利用事件相机输出事件流的低延时性质,融合存在运动模糊的亮度图像帧,提出基于事件相机的连续光流估计算法,提升了高速运动场景下的光流估计精度.实验结果表明,相比于现有的基于事件相机的光流估计算法,该算法在平均端点误差、平均角度误差和均方误差3个指标上,分别提升11%、45%和8%.在高速运动场景下,该算法能够准确重建出高速运动目标的连续光流,保证了存在运动模糊情况时,光流估计的精度.     

基于视频的实时血液流速计算方法*

针对探究移动医疗与智能手机相结合的新模式,提出了一种基于视频的能够实时计算血液流速的测量方法．该方法通过利用手机摄像头采集指端视频,经过高斯平滑处理及下采样实现图像压缩,进而对压缩后的图像进行相邻两帧之间光流速度的计算.另外,针对光照强度和运动速度变化较大的情况,提出了一种改进的光流算法．实验结果表明,该方法能方便、无创伤的检测出血液的实际移动速度,并可通过速度曲线反映出一个人的血管弹性的好坏．     

基于改进光流算法的机器人目标跟踪系统

基于计算机视觉的机器人运动目标检测与跟踪,就是建立起一种机器人视觉与电机驱动相关联的系统。光流算法在此类系统中有着广泛应用,但是求取所需稠密光流场的运算量过大是其明显的缺点。为减少计算时间,提高跟踪系统响应速度,引入均值平滑算法对传统H-S(Horn和Schunck)光流算法进行改进,并在此改进算法基础上建立起目标跟踪系统。通过此系统,机器人可以根据采集到的图像的光流场变化来检测运动目标,再通过对光流场的奇异值分解,对跟踪系统模型所需参数进行估算,并驱动机器人做出相应动作,保持对目标的跟踪,从而使机器人对周围环境变化做出及时、准确的动作。经过实验证明改进后的光流算法有效的减少了计算时间,增强跟踪系统的实时性能。     

一种新型的实效运动目标检测方法

提出一种新型的帧间差分光流的运动目标检测方法.该方法通过改进七帧差分和改进背景减除消除运动目标检测时出现的"空洞"和虚假目标;通过在光流计算方程加入权函数和引入通用动态图像模型建立新的光流约束条件,以解决常用光流场计算耗时长和亮度变化引起的约束方程不成立的问题,同时获取运动准确信息;最后通过阈值分割和形态学处理完成对目标的分割.实验对比分析表明,该方法能实现运动目标的准确快速检测与分割.     

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。     

基于改进光流算法和HMM的面部表情识别

提出了一种基于改进光流和HMM的面部表情识别算法,在Lucas-Kanade光流法中引入Hessian矩阵,可有效消除局部邻域中不可靠约束点,提高面部表情变化的光流场计算精度.同时采用GA算法对HMM参数重估,有效提高了HMM的分类性能.实验结果表明,该算法取得了较好的识别效果.     

一种基于光流场重建三维运动和结构的新方法

提出了一种基于稀疏光流场计算三维运动和结构的线性新方法 ,该方法综合视觉运动分析中的两类处理方法 ,选取图象中的角点作为特征点 ;并检测和跟踪图象序列中的角点 .记录检测到的角点在图象序列中的位移 ,在理论上证明了时变图象的光流场可以近似地用角点的位移场代替 ,从而得到时变图象的稀疏光流场 ;通过光流运动模型的建立 ,推导出由稀疏光流场重建三维物体运动和结构的线性方法 .通过用真实图象序列验证该算法 ,表明该算法取得了较好的效果     

基于宏块的Hs光流算法运动估计优化研究

光流场属于一种运动参数,它不仅能够为人们提供目标物体的运动信息,还能使人们对运动物体进行有效的识别与定位,从而使人们更加有效的对目标物体进行运动估计,这也使光流场在计算机视觉领域中有着非常重要的应用.Hs光流算法对于提高光流场质量有着决定性的影响,但其在对目标物体运动信息进行识别、跟踪与估计时,常常存在计算量过大、易受噪声影响等问题,这也使Hs光流算法难以满足人们的数据处理需求.为此,有必要对Hs光流算法进行相应的改进,以此提高光流场质量.本文通过对Hs光流算法在运动估计优化中存在的问题及其相关影响因素进行分析,提出了Hs光流算法的改进思路,在此基础上结合宏块运动估计算法对改进后的Hs光流算法运动估计优化进行深入的研究.     

一种运动目标多特征点的鲁棒跟踪方法研究

提出了一种基于特征光流分割和卡尔曼滤波估计的鲁棒性的运动目标跟踪方法。该方法具有很多特点：首先在特征光流的计算中采用由粗到细的层级匹配算法，因而能够计算大的运动速度和具有更好的匹配精度；其次采用了有效的遮挡判决算法，该算法综合利用了先验的信息，对噪声的干扰不敏感；最后建立了线性卡尔曼滤波模型，当特征点被遮挡或丢失时，能够预测它们的位置，这使得跟踪更具有主动性。实验表明，该方法具有高精度、快速跟踪和很好的鲁棒性。     

Accurate optical flow computation under non-uniform brightness variations

In this paper, we present a very accurate algorithm for computing optical flow with non-uniform brightness variations. The proposed algorithm is based on a generalized dynamic image model (GDIM) in conjunction with a regularization framework to cope with the problem of non-uniform brightness variations. To alleviate flow constraint errors due to image aliasing and noise, we employ a reweighted least-squares method to suppress unreliable flow constraints, thus leading to robust estimation of optical flow. In addition, a dynamic smoothness adjustment scheme is proposed to efficiently suppress the smoothness constraint in the vicinity of the motion and brightness variation discontinuities, thereby preserving motion boundaries. We also employ a constraint refinement scheme, which aims at reducing the approximation errors in the first-order differential flow equation, to refine the optical flow estimation especially for large image motions. To efficiently minimize the resulting energy function for optical flow computation, we utilize an incomplete Cholesky preconditioned conjugate gradient algorithm to solve the large linear system. Experimental results on some synthetic and real image sequences show that the proposed algorithm compares favorably to most existing techniques reported in literature in terms of accuracy in optical flow computation with 100% density.     

基于运动优化语义分割的变分光流计算方法

针对光照变化和大位移运动等复杂场景下图像序列变分光流计算的边缘模糊与过度分割问题,文中提出基于运动优化语义分割的变分光流计算方法.首先,根据图像局部区域的去均值归一化匹配模型,构建变分光流计算能量泛函.然后,利用去均值归一化互相关光流估计结果,获取图像运动边界信息,优化语义分割,设计运动约束语义分割的变分光流计算模型....     

Direct recovery of motion and shape in the general case by fixation

A direct method called fixation is introduced for solving the general motion vision problem: arbitrary motion relative to an arbitrary environment. This method results in a linear constraint equation that explicitly expresses the rotational velocity in terms of the translational velocity. The combination of this constraint equation with the brightness-change constraint equation solves the general motion vision problem. Avoiding correspondence and optical flow has been the motivation behind this direct method, which uses the image brightness information such as temporal and spatial brightness gradients directly. In contrast with previous direct methods, the fixation method does not put any severe restrictions on the motion or the environment. Moreover, the fixation method neither requires tracked images as its input nor uses tracking for obtaining fixated images. Instead, it introduces a pixel shifting process to construct fixated images for any arbitrary fixation point. This is done entirely in software without any use of camera motion for tracking     

图象光流场计算技术研究进展

时变图象光流场计算技术是计算机视觉中的重要研究内容，也是当今研究的热点问题。为了使人们对该技术有一个较全面的了解，因而对时变图象光流场计算技术的研究和进展做了较系统的论述，首先分别列举了灰度时变图象和彩色时变图象的光流场计算方法，并对这些方法进行了分类，然后总结了出目前图象光流场计算中存在的几个问题，最后对光流场计算技术的研究发展及其应用前景指出了一些可能的方向。     

Optical flow estimation for motion-compensated compression

The computation of optical flow within an image sequence is one of the most widely used techniques in computer vision. In this paper, we present a new approach to estimate the velocity field for motion-compensated compression. It is derived by a nonlinear system using the direct temporal integral of the brightness conservation constraint equation or the Displaced Frame Difference (DFD) equation. To solve the nonlinear system of equations, an adaptive framework is used, which employs velocity field modeling, a nonlinear least-squares model, Gauss–Newton and Levenberg–Marquardt techniques, and an algorithm of the progressive relaxation of the over-constraint. The three criteria by which successful motion-compensated compression is judged are 1.) The fidelity with which the estimated optical flow matches the ground truth motion, 2.) The relative absence of artifacts and “dirty window” effects for frame interpolation, and 3.) The cost to code the motion vector field. We base our estimated flow field on a single minimized target function, which leads to motion-compensated predictions without incurring penalties in any of these three criteria. In particular, we compare our proposed algorithm results with those from Block-Matching Algorithms (BMA), and show that with nearly the same number of displacement vectors per fixed block size, the performance of our algorithm exceeds that of BMA in all the three above points. We also test the algorithm on synthetic and natural image sequences, and use it to demonstrate applications for motion-compensated compression.     

Satellite derived estimates of the normal and tangential components of near-surface flow

Abstract

The determination of both the normal and tangential components of the total velocity is important in the study of cross- and along-isopycnal transport processes in the ocean. A pattern-matching method is used to determine objectively the total velocity. Sensitivity of this method to pattern and search tile sizes and to correlation threshold also is examined. Three methods for estimating the cross-isopycnal or normal component of the total flow are compared and discussed: Marr-Ullman, optical flow and minimum norm. It is also shown that optical flow and minimum norm are equivalent when the parameter a in the optical flow formulation is set to zero. The direct computation of the tangential component is not possible because it lies in the null-space of the solution set of the basic constraint equations used in velocity estimation methods which are based on the rate of change of image brightness (or temperature). A new method for indirectly estimating the tangential component of the total flow based on vector subtracting of the total flow and the normal component of flow is introduced. Several sequences of satellite images are analysed and the resulting total flow, normal component of flow, and the tangential component determined using this new method are consistent with motion inferred from edge maps. Recommendations are then made for the best normal component of flow to use in the determination of the tangential component.     

Reliable and Efficient Computation of Optical Flow

In this paper, we present two very efficient and accurate algorithms for computing optical flow. The first is a modified gradient-based regularization method, and the other is an SSD-based regularization method. For the gradient-based method, to amend the errors in the discrete image flow equation caused by numerical differentiation as well as temporal and spatial aliasing in the brightness function, we propose to selectively combine the image flow constraint and a contour-based flow constraint into the data constraint by using a reliability measure. Each data constraint is appropriately normalized to obtain an approximate minimum distance (of the data point to the linear flow equation) constraint instead of the conventional linear flow constraint. These modifications lead to robust and accurate optical flow estimation. We propose an incomplete Cholesky preconditioned conjugate gradient algorithm to solve the resulting large and sparse linear system efficiently. Our SSD-based regularization method uses a normalized SSD measure (based on a similar reasoning as in the gradient-based scheme) as the data constraint in a regularization framework. The nonlinear conjugate gradient algorithm in conjunction with an incomplete Cholesky preconditioning is developed to solve the resulting nonlinear minimization problem. Experimental results on synthetic and real image sequences for these two algorithms are given to demonstrate their performance in comparison with competing methods reported in literature.     

Shot Change Detection Using Scene-Based Constraint

A key step for managing a large video database is to partition the video sequences into shots. Past approaches to this problem tend to confuse gradual shot changes with changes caused by smooth camera motions. This is in part due to the fact that camera motion has not been dealt with in a more fundamental way. We propose an approach that is based on a physical constraint used in optical flow analysis, namely, the total brightness of a scene point across two frames should remain constant if the change across two frames is a result of smooth camera motion. Since the brightness constraint would be violated across a shot change, the detection can be based on detecting the violation of this constraint. It is robust because it uses only the qualitative aspect of the brightness constraint—detecting a scene change rather than estimating the scene itself. Moreover, by tapping on the significant know-how in using this constraint, the algorithm's robustness is further enhanced. Experimental results are presented to demonstrate the performance of various algorithms. It was shown that our algorithm is less likely to interpret gradual camera motions as shot changes, resulting in a significantly better precision performance than most other algorithms.     

Monocular scene flow estimation via variational method

Scene flow provides the 3D motion field of point clouds, which correspond to image pixels. Current algorithms usually need complex stereo calibration before estimating flow, which has strong restrictions on the position of the camera. This paper proposes a monocular camera scene flow estimation algorithm. Firstly, an energy functional is constructed, where three important assumptions are turned into data terms derivation: a brightness constancy assumption, a gradient constancy assumption, and a short time object velocity constancy assumption. Two smooth operators are used as regularization terms. Then, an occluded map computation algorithm is used to ensure estimating scene flow only on un-occluded points. After that, the energy functional is solved with a coarse-to-fine variational equation on Gaussian pyramid, which can prevent the iteration from converging to a local minimum value. The experiment results show that the algorithm can use three sequential frames at least to get scene flow in world coordinate, without optical flow or disparity inputting.     

Multimodal estimation of discontinuous optical flow using Markovrandom fields

The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported