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.     

Motion estimation and segmentation

In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing.     

Improving sub-pixel correspondence through upsampling

Many fundamental computer vision problems, including optical flow estimation and stereo matching, involve the key step of computing dense color matching among pixels. In this paper, we show that by merely upsampling, we can improve sub-pixel correspondence estimation. In addition, we identify the regularization bias problem and explore its relationship to image resolution. We propose a general upsampling framework to compute sub-pixel color matching for different computer vision problems. Various experiments were performed on motion estimation and stereo matching data. We are able to reduce errors by up to 30%, which would otherwise be very difficult to achieve through other conventional optimization methods.     

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.     

Real-time arm motion imitation for human–robot tangible interface

In this paper we deal with a remote meeting system with tangible interface, in which a robot is used as tangible avatar instead of a remote meeting partner. For realizing such system, it is a critical issue how the robot imitates human motions with natural and exact. So, we suggested a new method that human arm motion is captured with a stereo vision system and transferred to the robotic avatar with real-time. For capturing 3D arm motions based on markerless method, we proposed a new metaball-based method which was designed in order to have some robust and efficient properties: a modified iso-surface equation of metaball for overcoming local minima and a downsizing method of 3D point cloud for improving time complexity. With our meeting system, we have implemented our new algorithm and run at approximately 12–16 Hz. Also, its accuracy in motion capturing could be acceptable for robot motion generation.     

A Robust Approach for Structure from Planar Motion by Stereo Image Sequences

This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions.     

A Stochastic Approach for Blurred Image Restoration and Optical Flow Computation on Field Image Sequence

The blur in target images caused by camera vibration due to robot motion or hand shaking and by object(s) moving in the background scene is different to deal with in the computer vision system.In this paper,the authors study the relation model between motion and blur in the case of object motion existing in video image sequence,and work on a practical computation algorithm for both motion analysis and blut image restoration.Combining the general optical flow and stochastic process,the paper presents and approach by which the motion velocity can be calculated from blurred images.On the other hand,the blurred image can also be restored using the obtained motion information.For solving a problem with small motion limitation on the general optical flow computation,a multiresolution optical flow algoritm based on MAP estimation is proposed. For restoring the blurred image ,an iteration algorithm and the obtained motion velocity are used.The experiment shows that the proposed approach for both motion velocity computation and blurred image restoration works well.     

Motion and structure of four points from one motion of a stereo rigwith unknown extrinsic parameters

We describe an analytical method for recovering 3D motion and structure of four or more points from one motion of a stereo rig. The extrinsic parameters are unknown. The motion of the stereo rig is also unknown. Because of the exploitation of information redundancy, the approach gains over the traditional “motion and structure from motion” approach in that less features and less motions are required, and thus more robust estimation of motion and structure can be obtained. Since the constraint on the rotation matrix is not fully exploited in the analytical method, nonlinear minimization can be used to improve the result. We propose to estimate directly the motion and structure by minimizing the difference between the measured positions and the predicted ones in the image plane. Both computer simulated data and real data are used to validate the proposed algorithm, and very promising results are obtained     

Binocular matching constraints from motion

Structure from motion and structure from stereo are two vision cues for achieving 3D reconstruction. The two cues have complementary strengths; while 3D reconstruction is accurate but correspondence establishment is difficult in the stereo cue, the reverse is true in the motion cue. This paper addresses how to combine the two cues when a stereo pair of cameras are available to capture image data for 3D reconstruction. The work is distinct in that, in contrast with the previous ones, it is not to exploit the redundancy in the image data for boosting the reconstruction accuracy, but to make the two vision cues complementary, preserving their strengths and avoiding their weaknesses. A mechanism is introduced that allows dense motion correspondences in the two separate image streams be transferred to dense binocular correspondences across the image streams, so that 3D can be reconstructed from the latter and accurate reconstruction is possible even with short motions of the stereo rig. Both the stereo correspondences and the motion of the stereo rig are assumed to be unknown in this work. Experiments involving real image data are presented to indicate the feasibility and robustness of the approach.     

基于双序列比对算法的立体图像匹配方法*

在分析现有立体匹配方法的基础上,提出一种基于双序列比对算法的立体图像匹配方法。将立体图像对中同名极线上的像素灰度值看做是一对字符序列,使用基于动态规划思想的双序列比对算法对这些对字符序列进行匹配,以获取立体图像视差。为验证该方法的可行性和适用性,采用人脸立体图像对进行实验。实验结果表明,使用该方法进行立体图像匹配能获得光滑的、稠密的视差图。基于动态规划思想的双序列比对算法能够有效地解决立体图像匹配问题,从而为图像的立体匹配提供了一个实用有效的方法。     

一种新的基于散焦图像的深度恢复算法

由散焦图像求深度是计算机视觉中一个非常重要的课题。散焦图像中点的模糊程度随物体的深度而变化,因此可以利用散焦图像估计物体的深度信息,该方法不存在立体视觉和运动视觉中对应点的匹配问题,具有很好的应用前景。研究了一种基于散焦图像空间的深度估计算法：将散焦成像描述成热扩散过程,借助形变函数将两幅散焦图像扩张成一个散焦空间,再估计出形变参数,进而恢复物体的深度信息。最后利用实验验证了算法的有效性。     

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     

3-D motion estimation by integrating visual cues in 2-D multi-modal opti-acoustic stereo sequences

Object reconstruction and target-based positioning are among critical capabilities in deploying submersible platforms for a range of underwater applications, e.g., search and inspection missions. Optical cameras provide high-resolution and target details, but their utility becomes constrained by the visibility range. In comparison, high-frequency (MHz) 2-D sonar imaging systems introduced to the commercial market in recent years can image targets at distances of tens of meters in highly turbid waters.Where fair visibility permits optical imaging at reasonable quality, the integration with 2-D sonar data can enable better performance compared to deploying either system alone, and thus enabling automated operation in a wider range of conditions.We investigate the estimation of 3-D motion by exploiting the visual cues in optical and sonar video for vision-based navigation and 3-D positioning of submersible platforms. The application of structure from motion paradigm in this multi-modal imaging scenario also enables the 3-D reconstruction of scene features. Our method does not require establishing multi-modal association between corresponding optical and sonar features, but rather the tracking of features in the sonar and optical motion sequences independently. In addition to improving the motion estimation accuracy, another advantage of the proposed method includes overcoming the inherent ambiguities of monocular vision, e.g., the scale-factor ambiguity and dual interpretation of motion relative to planar scenes. We discuss how our solution can also provide an effective strategy to address the complex opti-acoustic stereo matching problem. Experiment with synthetic and real data demonstrate the advantages of our technical contribution.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.     

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....     

Stereoscopic Scene Flow Computation for 3D Motion Understanding

Building upon recent developments in optical flow and stereo matching estimation, we propose a variational framework for the estimation of stereoscopic scene flow, i.e., the motion of points in the three-dimensional world from stereo image sequences. The proposed algorithm takes into account image pairs from two consecutive times and computes both depth and a 3D motion vector associated with each point in the image. In contrast to previous works, we partially decouple the depth estimation from the motion estimation, which has many practical advantages. The variational formulation is quite flexible and can handle both sparse or dense disparity maps. The proposed method is very efficient; with the depth map being computed on an FPGA, and the scene flow computed on the GPU, the proposed algorithm runs at frame rates of 20 frames per second on QVGA images (320×240 pixels). Furthermore, we present solutions to two important problems in scene flow estimation: violations of intensity consistency between input images, and the uncertainty measures for the scene flow result.     

Semi-dense and robust image registration by shift adapted weighted aggregation and variational completion

This work presents a novel approach for both stereo and optical flow that deals with large displacements, depth/motion discontinuities and occlusions. The proposed method comprises two main steps. First, a novel local stereo matching algorithm is presented, whose main novelty relies in the block-matching aggregation step. We adopt an adaptive support weights approach in which the weight distribution favors pixels that share the same displacement with the reference one. State-of-the-art methods make the weight function depend only on image features. On the contrary, the proposed weight function depends additionally on the tested shift, by giving more importance to those pixels in the block-matching with smaller cost, as these are supposed to have the tested displacement. Moreover, the method is embedded into a pyramidal procedure to locally limit the search range, which helps to reduce ambiguities in the matching process and saves computational time. Second, the non-dense local estimation is filtered and interpolated by means of a new variational formulation making use of intermediate scale estimates of the local procedure. This permits to keep the fine details estimated at full resolution while being robust to noise and untextured areas using estimates at coarser scales. The introduced variational formulation as well as the block-matching algorithm are robust to illumination changes. We test our algorithm for both stereo and optical flow public datasets showing competitive results.     

Vision resolvability for visually servoed manipulation

This article introduces a sensor placement measure called vision resolvability. The measure provides a technique for estimating the relative ability of various visual sensors, including monocular systems, stereo pairs, multi-baseline stereo systems, and 3D rangefinders, to accurately control visually manipulated objects. The resolvability ellipsoid illustrates the directional nature of resolvability, and can be used to direct camera motion and adjust camera intrinsic parameters in real-time so that the servoing accuracy of the visual servoing system improves with camera-lens motion. The Jacobian mapping from task space to sensor space is derived for a monocular system, a stereo pair with parallel optical axes, and a stereo pair with perpendicular optical axes. Resolvability ellipsoids based on these mappings for various sensor configurations are presented. Visual servoing experiments demonstrate that vision resolvability can be used to direct camera-lens motion to increase the ability of a visually servoed manipulator to precisely servo objects. © 1996 John Wiley & Sons, Inc.     

Trinocular Stereo Using Shortest Paths and the Ordering Constraint

This paper describes a new algorithm for disparity estimation using trinocular stereo. The three cameras are placed in a right angled configuration. A graph is then constructed whose nodes represent the individual pixels and whose edges are along the epipolar lines. Using the well known uniqueness and ordering constraint for pair by pair matches simultaneously, a path with the least matching cost is found using dynamic programming and the disparity filled along the path. This process is repeated iteratively until the disparity at all the pixels are filled up. To demonstrate the effectiveness of our approach, we present results from real world images and compare it with the traditional line by line stereo using dynamic programming.     

立体视觉里程计中基于微粒群优化的初始运动估计和内点检测

初始运动估计和内点检测是影响立体视觉里程计定位精度的重要因素.目前,立体视觉里程计都采用基于3点线性运动估计的随机采样一致性(random sample consensus,RANSAC)方法.本文分析了随机采样一致性方法在初始运动估计中的性能:该方法对排除误匹配点是有效的,但在一定采样次数下采样到特征点提取误差和立体匹配误差都很小的匹配点的概率是很小的,所以通过该方法得到的初始运动参数和匹配内点不够精确。本文提出了采用微粒群优化的初始运动估计和内点检测新方法,该方法收敛速度快,搜索精确解的能力强,能够获得高精度的运动参数和匹配内点.立体视觉里程计仿真实验和真实智能车实验表明:和随机采样一致性方法相比,本文方法在运行时间、定位精度方面都更优越.