【作者】 卢宗庆；

【导师】 谢维信；

【作者基本信息】 西安电子科技大学 ， 信号与信息处理， 2007， 博士

【摘要】 运动图像分析是计算机视觉中的一项重要任务，其中的关键技术之一就是光流计算。光流不仅携带了被观察物体的运动信息，还携带有被观察物的三维结构，传感器参数，非刚性物体的局部弹性形变，甚至流体运动的矢量结构特征等丰富信息。通过光流我们可以了解物体许多重要的运动特性，在运动图像计算与分析中光流扮演着非常重要的角色。本文针对目前光流场计算与分析中存在的问题和不足，深入系统地进行了研究并取得了一定的成果，其中包括：对传统光流方程进行了改进；提出了解决运动边界问题的新思路和新方法；研究了基于光流的流体运动图像计算和分析方法；为了提高运动矢量特征描述的精度和可靠性，提出了基于时间关联约束的Multivector图像滤波方法；研究了基于光流场的序列图像运动特征描述；本文的成果对改善光流计算的精度和可靠性，克服光流场计算中存在的问题具有重要意义。对运动图像计算、分析领域的研究具有较高理论价值和应用价值。第一章论述了光流计算的研究意义，研究现状和存在问题，并介绍了本文的主要工作，以及论文内容安排。第二章为了减少光流方程的模型误差和改进计算的鲁棒性，论文提出了一种新的基于图像局部结构不变模型(LSCM)的光流计算方法。由于模型LSCM较传统的亮度常数模型(BCM)对于图像的光照变化不敏感，因此本文方法可以显著提高光流计算的鲁棒性。在本文方法中使用图像局部二阶张量来描述图像的局部结构特征。同时为了进一步提高计算精度，减少由于Taylor级数一阶近似所带来的模型误差，本文给出了一种双向运动计算策略。新策略既具有二阶Taylor级数展开的精度逼近，同时又避免了非线性方程的计算。第三章针对如何解决运动边界问题，本文提出了两种新的解决问题的思想。第一种思想采用概率选择策略来有效地解决不同运动区域的划分，同时结合局部更新计算来降低运动边界带来的干扰，以此提高运动边界处光流计算的精度。第二种思想从光流全局计算出发，直接通过非线性扩散反应方程来计算光流，这样避开了光流计算正则化方法中凸性和可微性的要求。同时本文分别设计了一致矢量增强扩散(CVED)和基于非线性滤波的扩散方式，来提高模型处理运动边界问题的能力。第四章研究了基于光流方法的流体运动图像计算(PIV)，将第三章基于非线性滤波的光流方法进行推广，并应用于流体运动图像。此外，本章将信号分析中最小均方滤波的思想应用于矢量场滤波，提出一种基于时间关联约束的滤波策略，实现对原有运动矢量的滤波和增强。考虑到流体运动矢量的散度(div)和旋度(curl)也是重要的计算和分析对象。为此本文借助Clifford代数将运动矢量、散度(div)和旋度(curl)构建成一多矢量(Multivector)，作为流体运动矢量特征的综合描述。通过Multivector滤波计算得到更加可靠的流体运动特征描述结果。第五章研究了基于光流场的序列图像运动特征描述。由于光流场的分辨率可以达到连续两帧的每一个像素，因此光流场为利用统计的方法进行图像运动特征描述提供了大量的冗余信息。文章在光流计算的基础上，研究了基于光流加速场统计信息的视频运动识别方法。给出了一种基于运动加速场的时空多分辨直方图(STMRH)，并利用STMRH进行动态纹理的运动特征辨识。为了提高算法的计算速度，论文还给出了一种简化的多网格快速光流计算方法。第六章对论文的工作和成果进行了总结和评述，同时简要给出了进一步的研究内容和方向。更多还原

【Abstract】 Motion image analysis is an important task of Computer Vision, and the optical flow computation is a key technique for motion image analysis. The optical flow is a convenient and useful image motion description, it contains rich 2D motion cues and is often used to recover 3D scene structure and 3D motion parameters of visual sensors. It also contains some important cues of elastic deformation of non-rigid bodies, and flow structure of fluid motion images. It can help us to know about many important motion characteristics of motion bodies by optical flow, It is undoubted that optical flow plays an important role in motion image computation and analysis.This thesis addresses some problems of optical flow computation and analysis including: increase the accuracy of optical flow computation, cope with the problems of motion boundary, propose a optical flow method for fluid motion image computation, give a vector filter based on temporal correlation constraints for the more accurate and reliable description of fluid motion characteristics, and analyze the motion patterns of video sequences by optical flow statistic. This research is significant for increasing the accuracy and reliability of optical flow computation, and it also has significant academic and practical value for motion images computation and analysis.The first chapter systematically introduces the background , the significance and the state of the art of optical flow computation, and indicates the limitations of traditional methods. This chapter also gives the structure and the contributions of this dissertation.In chapter 2, we propose a new optical flow method based on Local Structure Constancy Model (LSCM) instead of the traditional BCM for reducing the model error and increasing the robustness. Local image structure is less sensitive to illumination variation than intensity, which can increase the robustness of optical flow computation in real applications. Here we use two order structure tensors of an image to describe its local structure. A bi-directional computation scheme based on LSCM is also adopted for reducing the model error resulted from the linear Taylor’s expansion, which allows two order Taylor’s expansion of optical flow equation for a more accurate approximation and avoids nonlinear equation calculation.Chapter 3 addresses the issue of motion boundary problems to which two ideas are introduced. First, a probability-control selecting scheme is adopted, which divides the local neighbor into different regions with consistent motion vector. By a local refining step, the new method can preserve motion boundaries efficiently; Second, we view optical flow computation as a nonlinear diffusion process instead of an energy minimization process. It avoids the restrictions of convexity and differentiability required by normal regularization methods. We use a Coherence Vector Enhancing Diffusion (CVED) scheme and a bilateral filtering diffusion scheme to improve the efficiency of preserving motion boundaries, respectively.In chapter 4, an optical flow method based on nonlinear filtering scheme is proposed which is the extended version of the method based on bilateral filtering scheme proposed in chapter 3 and is applied to fluid motion images computation (PIV). In addition, a Least-squares (LS) vector-filtering scheme based on temporal correlation constraint is proposed for offline computation & analysis, which can help to suppress the computation noise and increase the reliability of fluid motion characteristics’ description. In view of the div and curl fields as the main objects of computation & analysis, we extend the proposed vector-filtering scheme to multivector space, which takes into account div and curl along with motion vectors. With the help of Clifford algebra, we construct multivectors by vectors, div and curl to characterize fluid motion. By a multivector filtering computation, we obtain the reliable description of fluid motion characteristics.The fifth chapter of the thesis is concerned with video motion analysis based on optical flow computation. From optical flow fields we can obtain the motion vector of each pixel per frame, which provides richly redundant information for statistic methods acting on optical flow fields. In this chapter we develop a statistics method for motion recognition based on motion accelerate fields. A spatio-temporal multiresolution histogram (STMRH) acting on motion accelerate fields as the motion character’s description of dynamic texture videos is proposed. In order to reduce computational consumption, a simplified multigrid computation scheme for optical flow is adopted.The final chapter gives the conclusion and the contributions of this dissertation, and indicates some related works that are yet remained and attractive for future research.更多还原