一种改进的全局光流估计方法

基于光流基本约束和平滑性约束条件的Horn-Schunck光流场佑计算法是图像运动估计的重要方法。但是,该方法存在在梯度值较小处运动参数估计不准确的问题;同时,现有的改进方法由于步及到可调参数的人工选取,并在阈值设置过高时容易在运动目标区域产生空洞,限制了光流法的应用。对光流基本约束项的权函数加以改进,给出了两种改进的光流估计算法。实验结果表明,改进算法能够在权函数阂值设置过高时降低对可靠光流的抑制,提高了算法的自适应性,为运动目标检测跟踪提供了有力条件。     

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.     

基于梯度光流场计算方法的一种改进

提出了一种新的改进光流场技术计算方法。它采用类似差分的方法从图像序列的多帧图像中检测出运动目标区域。通过代数运算减轻了Hom算法中全局约束对运动边界的影响。实验证明该方法是有效的。     

Computing optical flow with physical models of brightness variation

Although most optical flow techniques presume brightness constancy, it is well-known that this constraint is often violated, producing poor estimates of image motion. This paper describes a generalized formulation of optical flow estimation based on models of brightness variations that are caused by time-dependent physical processes. These include changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. With these models, we simultaneously estimate the 2D image motion and the relevant physical parameters of the brightness change model. The estimation problem is formulated using total least squares, with confidence bounds on the parameters. Experiments in four domains, with both synthetic and natural inputs, show how this formulation produces superior estimates of the 2D image motion     

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.     

一种自适应运动估值视频代码转换的新方法

针对窄带网络的视频信号传输问题,分析了传统视频代码转换帧速率转换时,由于运动矢量非最佳化所造成的图象质量下降的原因,并提出了一种基于量化误差的自自动化运动矢量模型,从而减小了搜索域,使最佳化输出运行矢量能进行快速运动估值;同时根据灰度系统理论,提出了一种有效的灰度预测搜索方法,另外,又根据DCT系数理论模型。采用自适应快速视频编码方法,进一步提高了编码速度,实验结果表明：该方法不仅改善了视频图象质量,而且计算复杂度也大大减小。     

一种基于线性亮度变化模型的鲁棒的光流算法

提出了一种鲁棒光流算法,用于计算光照强度、帧间运动速度及运动速度变化较大情况下的光流场。在梯度约束方程中嵌入了线性亮度变化模型,以提高大的光照强度变化下算法稳健性;将各向异性扩散方程引入空间方向平滑约束,以改善运动不连续处的流速计算精度,并依此建立了多尺度空间微分光流算法。参数的均衡化得到了线性尺度变化下的恒定能量函数。迭代运算引入运动补偿的概念,使亮度误差减小。实验结果表明,在光照强度和运动速度及速度变化较大时,本文算法具有很好的计算精度,并产生密度100%的光流场。     

一种基于模糊函数的自适应平滑约束图像复原算法

为了在抑制噪声的同时保护图像的细节,提出了一种基于模糊函数的自适应平滑约束图像复原算法。该算法首先用模糊函数对图像局部区域内线元素的数量和方向进行评价,然后根据评价结果使用不同的高通滤波器构造平滑约束条件,同时使平滑约束条件随着图像复原的迭代过程不断更新,以便自适应地在图像平坦区域抑制噪声,而在存在物体边界的区域则保护细节信息。对比实验表明,此方法具有更高的收敛速度,更好的客观评价指标和主观视觉效果。     

Robust reweighted MAP motion estimation

This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with these problems, a median operator and the concept of reweighted least squares (RLS) are applied to the MAP motion estimator, resulting in the reweighted robust MAP (RRMAP). The proposed RRMAP motion estimation algorithm is also generalized for multiple image frame cases. Computer simulation with various synthetic image sequences shows that the proposed algorithm reduces errors, compared to three existing robust motion estimation algorithms that are based on M-estimation, total least squares (TLS), and Hough transform. It is also observed that the proposed algorithm is statistically efficient and robust to additive Gaussian noise and impulse noise. Furthermore, the proposed algorithm yields reasonable performance for real image sequences     

位移场估计法在交通管理中的应用研究

智能交通系统中，车辆的行驶速度是重要的参数之一，为了快速获得车辆的行驶速度，提出了一种基于位移场的运动场估计计算法，该方法首先构造一个基于图象灰度的指标函数，再通过变分法中的欧拉方程分析和偏微分方程的数值解法，即可得到序列图象间的位移场；然后利用帧间位移场、帧间时间差、摄像机与车辆间的距离等参数相结合可计算出车辆的行驶速度，实验结果表明，与块匹配法相比，该方法不仅可得到更好的位移场，而且可快速获得车辆的行驶速度。     

Motion estimation methods and noisy phenomena

In an infrared surveillance system (which must detect remote sources and thus has a very low resolution) in an aerospace environment, the estimation of the cloudy sky velocity should lower the false alarm rate in discriminating the motion between various moving shapes by means of a background velocity map. The optical flow constraint equation, based on a Taylor expansion of the intensity function, is often used to estimate the motion for each pixel. One of the main problems in motion estimation is that, for one pixel, the real velocity cannot be found because of the aperture problem. Another kinematic estimation method is based on a matched filter [generalized Hough transform (GHT)]: it gives a global velocity estimation for a set of pixels. On the one hand we obtain a local velocity estimation for each pixel with little credibility because the optical flow is so sensitivity to noise; on the other hand, we obtain a robust global kinematic estimation, the same for all selected pixels. This paper aims to adapt and improve the GHT in our typical application in which one must discern the global movement of objects (clouds), whatever their form may be (clouds with hazy edges or distorted shapes or even clouds that have very little structure). We propose an improvement of the GHT algorithm by segmentation images with polar constraints on spatial gradients. One pixel, at timet, is matched with another one at timet + T, only if the direction and modulus of the gradient are similar. This technique, which is very efficient, sharpens the peak and improves the motion resolution. Each of these estimations is calculated within windows belonging to the image, these windows being selected by means of an entropy criterion. The kinematic vector is computed accurately by means of the optical flow constraint equation applied on the displaced window. We showed that, for small displacements, the optical flow constraint equation sharpens the results of the GHT. Thus a semi-dense velocity field is obtained for cloud edges. A velocity map computed on real sequences with these methods is shown. In this way, a kinematic parameter discriminates between a target and the cloudy background.     

基于SIFT特征匹配的视频稳像算法研究

为了解决运动情况下的视频图像去抖问题,本文提出了一种新型的去抖算法。首先利用SIFT算法提取出特征点,并利用RANSAC算法进行优化处理,进行图像的全局运动估计和全局运运补偿,实现视频图像的去抖功能。本文实现了以该方法为基本算法的原型系统,能够有效地增加视频图像的平滑度。     

基于深度匹配的由稀疏到稠密大位移运动光流估计

针对非刚性大位移运动场景的光流计算准确性与鲁棒性问题,提出一种基于深度匹配的由稀疏到稠密大位移运动光流估计方法.首先利用深度匹配模型计算图像序列相邻帧的初始稀疏运动场;其次采用网格化邻域支持优化模型筛选具有较高置信度的图像网格和匹配像素点,获得鲁棒的稀疏运动场;然后对稀疏运动场进行边缘保护稠密插值,并设计全局能量泛函优化求解稠密光流场.最后分别利用MPI-Sintel和KITTI数据库提供的测试图像集对本文方法和Classic+NL,DeepFlow, EpicFlow以及FlowNetS等变分模型、匹配策略和深度学习光流计算方法进行综合对比与分析,实验结果表明本文方法相对于其他方法具有更高的光流计算精度,尤其在非刚性大位移和运动遮挡区域具有更好的鲁棒性与可靠性.     

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.     

基于图像的动目标检测技术

本文研究了光流估计的基本算法，从运动分割的角度的提出了使用光流分割方案来解决复杂背景下的动目标检测问题。文中分析了标准光流估计的缺陷及其原因，并使用鲁棒性估计技术提高了光流估计算法的实用性。最后给出了动目标检测算法的仿真结果，表明利用图像的运动特征来实现目标检测是一种很有效的方法。     

Revised definition of optical flow: integration of radiometric andgeometric cues for dynamic scene analysis

Optical flow has been commonly defined as the apparent motion of image brightness patterns in an image sequence. In this paper, we propose a revised definition to overcome shortcomings in interpreting optical flow merely as a geometric transformation field. The new definition is a complete representation of geometric and radiometric variations in dynamic imagery. We argue that this is more consistent with the common interpretation of optical flow induced by various scene events. This leads to a general framework for the investigation of problems in dynamic scene analysis, based on the integration and unified treatment of both geometric and radiometric cues in time-varying imagery. We discuss selected models, including the generalized dynamic image model, for the estimation of optical flow. We show how various 3D scene information are encoded in, and thus may be extracted from, the geometric and radiometric components of optical flow. We provide selected examples based on experiments with real images     

利用平稳光流估计的海上视频去抖

目的 海上拍摄的视频存在大面积的无纹理区域,传统基于特征点检测和跟踪的视频去抖方法处理这类视频时往往效果较差。为此提出一种基于平稳光流估计的海上视频去抖算法。方法 该算法以层次化块匹配作为基础,引入平滑性约束计算基于层次块的光流,能够快速计算海上视频的近似光流场;然后利用基于平稳光流的能量函数优化,实现海上视频的高效去抖动。结果 分别进行了光流估计运行时间对比、视频稳定运行时间对比和用户体验比较共3组实验。相比于能处理海上视频去抖的SteadyFlow算法,本文的光流估计算法较SteadFlow算法的运动估计方法快10倍左右,整个视频去抖算法在处理速度上能提升70%以上。本文算法能够有效地实现海上视频去抖,获得稳定的输出视频。结论 提出了一种基于平稳光流估计的海上视频去抖算法,相对于传统方法,本文方法更适合处理海上视频的去抖。     

Estimate Large Motions Using the Reliability-Based Motion Estimation Algorithm

Detecting and estimating motions of fast moving objects has many important applications. However, most existing motion estimation techniques have difficulties in handling large motions in the scene. In this paper, we extend our recently proposed reliability-based stereo vision technique to solving large motion estimation problem. Compared with our stereo vision approach, the new algorithm removes the constant penalty assumption and explicitly enforces the inter-scanline consistency constraint. The resulting algorithm can handle sequences that contain large motions and can produce optical flows with 100% density over the entire image domain. The experimental results indicate that it can generate more accurate optical flows than existing approaches.     

Generalized optical flow in the scale space

Scale space is a natural way to handle multi-scale problems. Yang and Ma have considered the correspondence between scales, and proposed optical flow in the scale space. In this paper, we generalized Yang and Ma’s work to generic images. We first generalize the Horn–Schunck algorithm to multi-dimensional multi-channel image sequence. Since the global smoothness constraint for regularization is no longer suitable in general cases, we introduce localized smoothness regularization. In scale space optical flow, points in original image trends to aggregate at a large scale, so we introduce aggregation density as an additional smoothness coefficient. At last, we apply the proposed methods to color images and 3D images.     

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。