流体运动估计光流算法研究综述

对流体图像序列进行运动分析一直是流体力学、医学和计算机视觉等领域的重要研究课题。从图像对中提取的密集精确的速度矢量场能够为许多领域提供有价值的信息,基于光流法的流体运动估计技术因其独特的优势成为一个有前途的方向。光流法可以获得具有较高分辨率的密集速度矢量场,在小尺度精细结构的测量上有所改进,弥补了基于相关分析法的粒子图像测速技术的不足。此外,光流方法还可以方便的引入各种物理约束,获得较为符合流体运动特性的运动估计结果。为了全面反映基于光流法的流体运动估计算法的研究进展,本文在广泛调研相关文献的基础上,对国内外具有代表性的论文进行了系统阐述。首先介绍了光流法的基本原理,然后将现有算法按照要解决的突出问题进行分类：结合流体力学知识的能量最小化函数,提高对光照变化的鲁棒性,大位移估计和消除异常值。对每类方法,从问题解决过程的角度予以介绍,分析了各类突出问题中现有算法的特点和局限性。最后,总结分析了流体运动估计技术当前面临的问题和挑战,并对未来基于光流法的运动估计算法的研究方向和研究重点进行了展望。     

基于MAP框架的时空联合自适应视频序列超分辨率重建

超分辨率重建在视频监控、高清晰度电视、遥感图像、医学图像处理等领域具有广阔的应用前景. 最大后验估计(maximum a posteriori, MAP)法是普遍采用的一种超分辨率重建方法. 针对传统MAP法存在的局限性, 本文提出了一种基于MAP框架的时空联合自适应视频序列超分辨率重建算法. 时空联合自适应机制的引入使得算法在保持边缘的同时可减小错误运动估计矢量对重建图像质量的影响. 实验结果表明, 算法具有重建质量好、边缘保持能力强、收敛速度快等特点.     

基于一种优化的配准算法的图像超分辨率重建

提出了一种优化的图像配准算法.该算法充分结合了时域基于全局运动模型的配准算法和频域基于傅立叶变换的配准算法,并运用到超分辨重建中.假设序列低分辨率图像之间存在旋转和平移,首先运用全局配准法求出旋转参数,对图像进行旋转补偿,然后对补偿后的图像用频域配准法估计平移参数,针对估计出的旋转参数和平移参数对序列低分辨率图像进行超分辨率图像重建,实验表明该方法能较有效地提高图像分辨的效果,并且本文还阐述了低分辨率图像帧数的影响.     

图像序列运动估计中经典块匹配算法研究

对图像序列运动估计中的穷尽搜索法、三步法、新三步法和菱形法等块匹配算法的基本思想、算法描述、搜索模板以及算法性能进行分析和研究,并在实验仿真中,采用平均每块搜索点数和平均峰值信噪比PSNR为衡量指标,测试了在三种典型的视频序列运动估计中每种算法的搜索速度和效果,得出菱形法综合性能更为优越的结论。     

一种基于皇后模板抽样的全局运动估计方法

在目标跟踪、视频编码等领域都需要对全局运动进行快速准确的估计.针对既有摄像机运动又有前景物体运动的视频图像序列的全局运动估计问题,提出了一种新的全局运动估计算法.该新算法先采用3层金字塔进行多分辨率计算;然后在每层运算中都采用基于块的去外点法来有效去除外点.为了提高运算速度,在金字塔下面两层中还分别采用四皇后、八皇后模板抽取背景像素,只有被抽到的像素才参与迭代计算.通过对比试验可以看到,该算法在不降低精度的情况下,大大提高了估计速度,达到了实时性要求.     

基于掩模金字塔的高精度全局运动估计算法

在视频序列的全局运动估计中,前景运动对象的存在常常会大幅度地降低估计的精度,为此提出一种对前景对象自适应的高精度全局运动估计算法.该算法以像素块为单位,利用块内外点的比重判定前景区域,同时引入马尔可夫聚类方法进行后处理,有效地提高了运动对象的定位精度;通过对目标函数引入权重系数增强对残差的鲁棒性,以进一步提高算法的估计精度.此外,基于像素掩模的3层金字塔构建序列图像,并将改进的梯度方法引入到优化过程中,提高了算法的实时性.对不同运动类型的标准视频序列的实验结果表明,该算法有效地提高了全局运动估计的精度和速度.     

基于梯度投影法的电子稳像算法

灰度投影法在灰度值单一、对比度差的图像序列中的运动估计不精准,易造成误差。针对该问题,提出一种基于梯度投影法的电子稳像算法。该算法根据梯度帧内变化剧烈、帧间变化稳定、梯度投影曲线变化明显等特点,利用图像的梯度分布情况进行运动估计。实验结果表明,该算法可提高微光图像序列的帧间运动估计精度。     

基于自适应Kalman预测器的运动估计算法

利用图像序列估计目标运动速度是机器人视觉中的一项重要研究内容。它应用在机器人操作、导航、视觉跟踪等多项领域中。这些应用一般均要求运动估计算法具有较好的实时性和抗噪能力。卡尔曼滤波器和预测器正符合上述要求。该文基于运动图像的仿射模型，探讨从序列图像中预测目标三维平动速度的卡尔曼预测算法。首先建立运动目标的“当前”统计模型，然后根据运动图像的仿射模型找出图像运动参数与目标三维速度间的关系(图像运动参数由目标图像的几何矩计算获得)。最后结合自适应卡尔曼滤波和卡尔曼一步预测算法设计自适应卡尔曼一步预测器。为减轻预测器的发散性，对初始状态进行估计。仿真结果表明，基于“当前”统计模型和运动图像仿射模型设计出的自适应卡尔曼一步预测器具有较高的精度。     

超声图像序列运动估计相似度函数研究

研究超声图像序列运动估计的相似度函数性能问题,超声图像易受斑点噪声恶化,造成图像质量差。传统的相似度函数的选择影响运动估计精度和应用效果。为评估不同相似度函数性能,通过对像素强度和超声散斑特性的两类相似度函数在仿真的超声图像序列上用图像块匹配方法进行分析,并采用运动矢量场角度误差和终块误差法量化评估其在超声图像序列上进行运动估计的优劣。仿真结果表明,基于超声散斑特性的相似度函数能更准确地对具有超声图像特性的仿真图像序列进行运动估计,结果对不同超声研究领域中运动估计及相似度函数的选择具有重要的参考价值。     

基于立体视觉的球形机器人定位方法

针对球形机器人定位问题,提出了基于立体视觉的球形机器人定位方法.通过双目相机采集环境图像序列,提取Shi-Tomasi特征点,计算尺度不变特征变换(SIFT)特征描述符,并利用欧氏距离进行立体匹配;通过KLT算法进行特征点跟踪;采用解析法求解机器人在前后帧图像之间的位姿变化量;同时采用特征点筛选、RANSAC算法和卡尔曼滤波等方法,提高运动估计的准确性和鲁棒性.实验结果验证了所提出方法的可行性.     

Dynamic imagery modeling and motion estimation using weak formulations

Basic research into the modeling and analysis of image sequence dynamics is presented. A distributed parameter system (DPS) framework and the concept of weak solutions are used to develop image motion estimation algorithms. These algorithms represent a region-oriented (as opposed to point-by-point) approach to image motion analysis which is theoretically justifiable, computationally advantageous, and leads to interesting extensions. Particularly noteworthy is the use of weak solution-based motion features to obtain static image structural information and multiple object motion estimates. Experimental results confirm the validity and accuracy of the approach. Future research topics are described.     

New techniques on deformed image motion estimation and compensation

In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.     

Outward-looking circular motion analysis of large image sequences

This work presents a novel and simple method of analyzing the motion of a large image sequence captured by a calibrated outward-looking video camera moving on a circular trajectory for large-scale environment applications. Previous circular motion algorithms mainly focus on inward-looking turntable-like setups. They are not suitable for outward-looking motion where the conic trajectory of corresponding points degenerates to straight lines. The circular motion of a calibrated camera essentially has only one unknown rotation angle for each frame. The motion recovery for the entire sequence computes only one fundamental matrix of a pair of frames to extract the angular motion of the pair using Laguerre's formula and then propagates the computation of the unknown rotation angles to the other frames by tracking one point over at least three frames. Finally, a maximum-likelihood estimation is developed for the optimization of the whole sequence. Extensive experiments demonstrate the validity of the method and the feasibility of the application in image-based rendering.     

基于动态图像序列的下肢运动参数测量

针对人体下肢运动参数的检测问题，提出了基于动态图像序列的下肢运动测量方法。首先对成像系统进行了分析，然后根据所得的图像序列，提出了图像帧间特征点的对应、下肢的运动及结构参数估计等问题的处理方法，实验结果反映了该方法的有效性。     

The precision of 3-D parameters in correspondence-based techniques:the case of uniform translational motion in a rigid environment

A general method of calculating the precision of environmental parameters which differs from conventional methods by finding analytical results, and which does so using only information in the image is discussed. This method called uncertainty-analysis, is applied to the case of first-order-error-based motion algorithms for determining environmental depth by matching corresponding points between two or more frames of a dynamic image sequence. How to calculate the uncertainty of in predicted values for the depth is shown, as well as how to calculate the search region for further instances of the point in subsequent frames, based only on information present in the image. The analysis is applied to a real image sequence with good results     

Fusing Visual and Inertial Sensing to Recover Robot Ego‐motion

A method for estimating mobile robot ego‐motion is presented, which relies on tracking contours in real‐time images acquired with a calibrated monocular video system. After fitting an active contour to an object in the image, 3D motion is derived from the affine deformations suffered by the contour in an image sequence. More than one object can be tracked at the same time, yielding some different pose estimations. Then, improvements in pose determination are achieved by fusing all these different estimations. Inertial information is used to obtain better estimates, as it introduces in the tracking algorithm a measure of the real velocity. Inertial information is also used to eliminate some ambiguities arising from the use of a monocular image sequence. As the algorithms developed are intended to be used in real‐time control systems, considerations on computation costs are taken into account. © 2004 Wiley Periodicals, Inc.     

实时跟踪系统中运动人体图像分割

在视频序列的人体运动分析中，实时分割出运动的人体，是研究的起始关键步骤。该文在简单的运用差图像算法进行人体运动检测的基础上，结合直方图自动阈值分割和数学形态学的算法来完成运动人体的精确分割。实验结果表明上述算法对噪声抑制和人体图像断裂处填充都是有效的，能够实时分割完成运动人体的视频图像。     

Detection of swimmer using dense optical flow motion map and intensity information

A vision-based system that can locate individual swimmers and recognize the activities is applicable for swimming gait analysis, drowning event detection, etc. The system relies on accurate detection of swimmer’s body parts such as head and upper limbs. The swimmer detection problem can be regarded as background subtraction. Swimmer detection in the aquatic environment is very difficult due to a dynamic background with water ripples, splashes, specular reflections, etc. This paper presents a swimmer detection method which utilizes both local motion and intensity information estimated from the image sequence. Local motion information is obtained by computing dense optical flow and periodogram. We adopt a heuristic approach to generate a motion map characterizing the local motions (random/stationary, ripple or swimming) of image pixels over a short duration. Intensity information is modeled as a mixture of Gaussians. Finally, using the motion map and the Gaussian models, swimmers are detected in each video frame. We test the method on video sequences captured at daytime, and nighttime, and of different swimming styles (breaststroke, freestyle, backstroke). Our method can detect swimmers much better than that using intensity information alone. In addition, we compare our method with existing algorithms—codebook model and self-organizing artificial neural networks. The methods are tested on publicly available video sequence and our swimming video sequence. We show through the quantitative measures the superiority of our method.     

中值直方图均衡的动态场景多曝光图像融合算法

为解决动态场景下多曝光融合图像出现鬼影的问题,提出一种新的动态多曝光图像融合算法。引入中值图像均衡对输入图像和参考图像的直方图进行处理,将获取的图像对做差分,并对差分图进行阈值分割和形态学优化得到运动权重图。中值直方图均衡可以为一对图像分配相同的直方图,同时保持其灰度动态,因此对多曝光图像对调整其亮度差异,有利于运动区域检测的准确性。通过强度映射函数将参考图像分别映射为各个输入图像的亮度,并将输入图像的运动区域替换为参考图像的一部分,得到具有亮度过渡自然的图像序列。在此基础上,对静态图像序列进行融合得到最终的融合图像。实验结果表明,该算法可有效地避免鬼影现象,且能够获得细节丰富、视觉效果良好的高动态范围图像,经该算法融合后的图像在标准差、边缘强度、相关系数和动态场景结构一致4个指标上与DGF、FMSD等算法相比具有明显的优势。     

基于图像多尺度运动特征的模糊分割算法

从运动分析的角度出发,对所研究的图像序列,提出了一种基于空域－时域连续小波变换（CWT）的模糊分割算法。运用可分离的小波函数作为滤波器来提取不同尺度上的运动参数,由不尺度上的运动特征参数构成特征参数集,然后运用快速模糊聚类算法实现基于运动特征的图象分割。该分割算法不仅对噪声具有较强的适应性,而且也极大地提高了图像分割的速度,同时使得分割结果能够更加准确地反映运动物体的特征。