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.     

Three-dimensional scene flow

Just as optical flow is the two-dimensional motion of points in an image, scene flow is the three-dimensional motion of points in the world. The fundamental difficulty with optical flow is that only the normal flow can be computed directly from the image measurements, without some form of smoothing or regularization. In this paper, we begin by showing that the same fundamental limitation applies to scene flow; however, many cameras are used to image the scene. There are then two choices when computing scene flow: 1) perform the regularization in the images or 2) perform the regularization on the surface of the object in the scene. In this paper, we choose to compute scene flow using regularization in the images. We describe three algorithms, the first two for computing scene flow from optical flows and the third for constraining scene structure from the inconsistencies in multiple optical flows.     

Multi-body Factorization with Uncertainty: Revisiting Motion Consistency

Dynamic analysis of video sequences often relies on the segmentation of the sequence into regions of consistent motions. Approaching this problem requires a definition of which motions are regarded as consistent. Common approaches to motion segmentation usually group together points or image regions that have the same motion between successive frames (where the same motion can be 2D, 3D, or non-rigid). In this paper we define a new type of motion consistency, which is based on temporal consistency of behaviors across multiple frames in the video sequence. Our definition of consistent “temporal behavior” is expressed in terms of multi-frame linear subspace constraints. This definition applies to 2D, 3D, and some non-rigid motions without requiring prior model selection. We further show that our definition of motion consistency extends to data with directional uncertainty, thus leading to a dense segmentation of the entire image. Such segmentation is obtained by applying the new motion consistency constraints directly to covariance-weighted image brightness measurements. This is done without requiring prior correspondence estimation nor feature tracking.     

基于优化的DTW算法的人体运动数据检索

随着大量三维人体运动数据库的建立,使得在数据库中实现基于内容的三维人体运动检索面临着诸多困难,文中提出一种分阶段的动态时间变形(DTW)优化算法的人体运动数据检索技术,可有效检索出逻辑上相似的运动。该算法首先对齐两个运动序列的坐标位置,基于窗口距离构造距离矩阵。其次采用基于全局和局部约束的DTW优化算法进行相似度匹配,得到两个运动间的对应关系。最后通过归一化相似度和DTW平均距离分阶段判断运动的相似性。实验结果表明,分阶段的DTW优化算法在提高效率的同时对长度不等的运动能取得较好的检索结果。     

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.     

3-D recovery of human motion by mobile stereo cameras

A method is described which recovers the 3-D shape of deformable objects, particularly human motions, from mobile stereo images. In the proposed technique, camera calibration is not required when taking images. Existing optical 3-D modeling systems must employ calibrated cameras that are set at fixed positions. This inevitably puts constraints on the range of the movement of an object. In the proposed method, multiple mobile cameras take images of a deformable object moving freely, and its 3-D model is reconstructed from the video image streams obtained. The advantages of the proposed method include the fact that the cameras employed are calibration-free, and that the image-taking cameras can move freely. The theory is described, and the performance is shown by an experiment on 3-D human motion modeling in an outdoor environment. The accuracy of the 3-D model obtained is evaluated and a discussion is given. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Efficient local transformation estimation using Lie operators

Conventional translation-only motion estimation algorithms cannot cope with transformations of objects such as scaling, rotations and deformations. Motion models characterizing non-translation motions are thus beneficial as they offer more accurate motion estimation and compensation. In this paper, we introduce low-complexity transformation estimation methods with four motion models based on Lie operators, which are linear operators that have found applications in optical character recognitions. We show that individual Lie operators are capable of capturing small degrees of object transformations. We propose an efficient local transformation estimation algorithm in order to further improve the accuracy of the translation-only estimation by integrating all four motion models. Simulations with an MPEG-2 video codec on two video sequences show that the proposed transformation estimation approach can noticeably improve the motion compensation performance of the translation-only method by achieving higher PSNR (peak signal-to-noise ratio) values for the predicted frames, with only a small fraction of the complexity required by the translation motion search.     

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....     

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     

Motion segmentation and pose recognition with motion history gradients

This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program. Accepted: 13 August 2001     

Rolling and swaying motion estimation for a mobile robot by using omnidirectional optical flows

Described here is a method for estimating rolling and swaying motions of a mobile robot using optical flow. We have proposed an image sensor with a hyperboloidal mirror for the vision-based navigation of a mobile robot. Its name is HyperOmni Vision. The radial component of optical flow in HyperOmni Vision has a periodic characteristic. The circumferential component of optical flow has a symmetric characteristic. The proposed method makes use of these characteristic to estimate robustly the rolling and swaying motion of the mobile robot. Correspondence to: Y. Yagi e-mail: y-yagi@sys.es.osaka-u.ac.jp     

一种基于图像相位变化的动画算法

W.T.Freeman等人提出了基于图像相位变化的动画算法。该文利用这种动画算法,结合光流场,在一个动画或视频的连续两帧图像之间实现了永不停止的动画。在灰度图像上实现这种动画的基础上,针对这种无移动动画对图像质量的损害,进行了灰度拉伸等后期处理,提高了动画的图像质量。另外,把这种算法扩展到彩色图像,并采用lαβ颜色空间,得到了更好的效果。最后,把这种动画通过纹理映射,应用到三维场景中。     

Design and Use of Linear Models for Image Motion Analysis

Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic motion features such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.     

Utilization of stereo disparity and optical flow information for the computer analysis of human interactions

We propose a system that simultaneously utilizes the stereo disparity and optical flow information of real-time stereo grayscale multiresolution images for the recognition of objects and gestures in human interactions. For real-time calculation of the disparity and optical flow information of a stereo image, the system first creates pyramid images using a Gaussian filter. The system then determines the disparity and optical flow of a low-density image and extracts attention regions in a high-density image. The three foremost regions are recognized using higher-order local autocorrelation features and linear discriminant analysis. As the recognition method is view based, the system can process the face and hand recognitions simultaneously in real time. The recognition features are independent of parallel translations, so the system can use unstable extractions from stereo depth information. We demonstrate that the system can discriminate the users, monitor the basic movements of the user, smoothly learn an object presented by users, and can communicate with users by hand signs learned in advance. Received: 31 January 2000 / Accepted: 1 May 2001 Correspondence to: I. Yoda (e-mail: yoda@ieee.org, Tel.: +81-298-615941, Fax: +81-298-613313)     

应用深度光学应变特征图的人脸活体检测

目的 随着人脸识别系统应用的日益广泛,提高身份认证的安全性,提升人脸活体检测的有效性已经成为迫切需要解决的问题。针对活体检测中真实用户的照片存在的人脸欺骗问题,提出一种新的解决照片攻击的人脸活体检测算法。方法 利用局部二值模式LBP（local binary pattern）、TV-L1（total variation regularization and the robust L1 norm）光流法、光学应变和深度网络实现的人脸活体检测方法。对原始数据进行预处理得到LBP特征图;对LBP特征图提取光流信息,提高对噪声适应的鲁棒性;计算光流的导数得到图像的光学应变图,以表征相邻两帧之间的微纹理性质的微小移动量;通过卷积神经网络模型（CNN）将每个应变图编码成特征向量,最终将特征向量传递给长短期记忆LSTM（long short term memory）模型进行分类,实现真假人脸的判别。结果 实验在两个公开的人脸活体检测数据库上进行,并将本文算法与具有代表性的活体检测算法进行对比。在南京航空航天大学（NUAA）人脸活体检测数据库中,算法精度达到99.79%;在Replay-attack数据库中,算法精度达到98.2%,对比实验的结果证明本文算法对照片攻击的识别更加准确。结论 本文提出的针对照片攻击的人脸活体检测算法,融合光学应变图像和深度学习模型的优点,使得人脸活体检测更加准确。     

基于光流法的迭代反投影超分辨率重构算法

为了提高重构图像或者视频的分辨率．提出把新型的基于光流法的图像配准算法应用于迭代反投影（IBP）超分辨率算法中。在所提出的方法中．基于光流法的图像配准算法用来提高图像配准的准确性。首先,为了得到像素级别的运动矢量．基于光流法的图像配准算法被用于估计图像间的运动矢量。以得到更加准确的运动矢量矩阵。接着,利用所获得的运动矢量矩阵结合迭代反投影算法重构高分辨率的图像。同时．由于基于光流法的图像配准能够很好地估计视频图像间的运动．所提出的方法同样适用于视频图像的超分辨。实验结果表明．提出的方法对于图像或者视频的超分辨率效果．在主观效果和客观评价上都有一定的提升。     

Image Sequence Stabilization in Real Time

This paper describes a method ofstabilizingimage sequences obtained by a camera carried by a ground vehicle. The motion of the vehicle can usually be regarded as consisting of a desired smooth motion combined with an undesired non-smooth motion that includes impulsive or high-frequency components. The goal of the stabilization process is to correct the images so that they are approximately the same as the images that would have been obtained if the motion of the vehicle had been smooth.We analyse the smooth and non-smooth motions of a ground vehicle and show that only the rotational components of the non-smooth motion have significant perturbing effects on the images. We show how to identify image points at which rotational image flow is dominant, and how to use such points to estimate the vehicle's rotation. Finally, we describe an algorithm that fits smooth (ideally, piecewise constant) rotational motions to these estimates; the residual rotational motion can then be used to correct the images. We have obtained good results for several image sequences obtained from a camera carried by a ground vehicle moving across bumpy terrain.