摄像机旋转运动下的快速目标检测算法

论文提出了一种摄像机旋转运动下的快速目标检测算法。首先为图像的全 局运动建立旋转参数模型,然后基于运动预测在相邻帧之间建立SIFT 特征点对,利用 RANSAC 去除外点的影响,结合最小二乘法求解全局运动参数进行运动补偿,基于残差图 像的更新策略实时更新特征点集,以适应背景的变化,最后使用帧差法获得运动目标。该算 法不仅保持了SIFT 本身的优越性能,而且极大地提高了检测速度。实验结果表明该算法可 以实时准确的检测出运动目标。     

Automatic feature point extraction and tracking in image sequences for arbitrary camera motion

An automatic egomotion compensation based point correspondence algorithm is presented. A basic problem in autonomous navigation and motion estimation is automatically detecting and tracking features in consecutive frames, a challenging problem when camera motion is significant. In general, feature displacements between consecutive frames can be approximately decomposed into two components: (i) displacements due to camera motion which can be approximately compensated by image rotation, scaling, and translation; (ii) displacements due to object motion and/or perspective projection. In this paper, we introduce a two-step approach: First, the motion of the camera is compensated using a computational vision based image registration algorithm. Then consecutive frames are transformed to the same coordinate system and the feature correspondence problem is solved as though tracking moving objects for a stationary camera. Methods of subpixel accuracy feature matching, tracking and error analysis are introduced. The approach results in a robust and efficient algorithm. Results on several real image sequences are presented.The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Engineer Topographic Laboratories under Contract DACA 76-92-C-0009 is gratefully acknowledged.     

定位图像匹配尺度与区域的摄像机位姿实时跟踪

目的 提出一种定位图像匹配尺度及区域的有效算法,通过实现当前屏幕图像特征点与模板图像中对应尺度下部分区域中的特征点匹配,实现摄像机对模板图像的实时跟踪,解决3维跟踪算法中匹配精度与效率问题。方法 在预处理阶段,算法对模板图像建立多尺度表示,各尺度下的图像进行区域划分,在每个区域内采用ORB（oriented FAST and rotated BRIEF）方法提取特征点并生成描述子,由此构建图像特征点的分级分区管理模式。在实时跟踪阶段,对于当前摄像机获得的图像,首先定位该图像所对应的尺度范围,在相应尺度范围内确定与当前图像重叠度大的图像区域,然后将当前图像与模板图像对应的尺度与区域中的特征点集进行匹配,最后根据匹配点对计算摄像机的位姿。结果 利用公开图像数据库（stanford mobile visual search dataset）中不同分辨率的模板图像及更多图像进行实验,结果表明,本文算法性能稳定,配准误差在1个像素左右;系统运行帧率总体稳定在2030 帧/s。结论 与多种经典算法对比,新方法能够更好地定位图像匹配尺度与区域,采用这种局部特征点匹配的方法在配准精度与计算效率方面比现有方法有明显提升,并且当模板图像分辨率较高时性能更好,特别适合移动增强现实应用。     

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.     

Matching point features under small nonrigid motion

This paper describes a method for matching point features between images of objects that have undergone small nonrigid motion. Feature points are assumed to be available and, given a properly extracted set of feature points, a robust matching is established under the condition that the local nonrigid motion of each point is restricted to a circle of radius δ, where δ is not too large. This is in contrast to other techniques for point matching which assume either rigid motion or nonrigid motion of a known kind. The point matching problem is viewed in terms of weighted bipartite graph matching. In order to account for the possibility that the feature selector can be imprecise, we incorporate a greedy matching strategy with the weighted graph matching algorithm. Our algorithm is robust and insensitive to noise and missing features. The resulting matching can be used with image warping or other techniques for nonrigid motion analysis, image subtraction, etc. We present our experimental results on sequences of mammograms, images of a deformable clay object and satellite cloud images. In the first two cases we provide quantitative comparison with known ground truth.     

利用运动线索的单目深度测量

目的 传统的单目视觉深度测量方法具有设备简单、价格低廉、运算速度快等优点,但需要对相机进行复杂标定,并且只在特定的场景条件下适用。为此,提出基于运动视差线索的物体深度测量方法,从图像中提取特征点,利用特征点与图像深度的关系得到测量结果。方法 对两幅图像进行分割,获取被测量物体所在区域;然后采用本文提出的改进的尺度不变特征变换SIFT（scale-invariant feature transtorm）算法对两幅图像进行匹配,结合图像匹配和图像分割的结果获取被测量物体的匹配结果;用Graham扫描法求得匹配后特征点的凸包,获取凸包上最长线段的长度;最后利用相机成像的基本原理和三角几何知识求出图像深度。结果 实验结果表明,本文方法在测量精度和实时性两方面都有所提升。当图像中的物体不被遮挡时,实际距离与测量距离之间的误差为2.60%,测量距离的时间消耗为1.577 s;当图像中的物体存在部分遮挡时,该方法也获得了较好的测量结果,实际距离与测量距离之间的误差为3.19%,测量距离所需时间为1.689 s。结论 利用两幅图像上的特征点来估计图像深度,对图像中物体存在部分遮挡情况具有良好的鲁棒性,同时避免了复杂的摄像机标定过程,具有实际应用价值。     

A Theory of Specular Surface Geometry

A theoretical framework is introduced for the perception of specular surface geometry. When an observer moves in three-dimensional space, real scene features such as surface markings remain stationary with respect to the surfaces they belong to. In contrast, a virtual feature which is the specular reflection of a real feature, travels on the surface. Based on the notion of caustics, a feature classification algorithm is developed that distinguishes real and virtual features from their image trajectories that result from observer motion. Next, using support functions of curves, a closed-form relation is derived between the image trajectory of a virtual feature and the geometry of the specular surface it travels on. It is shown that, in the 2D case, where camera motion and the surface profile are coplanar, the profile is uniquely recovered by tracking just two unknown virtual features. Finally, these results are generalized to the case of arbitrary 3D surface profiles that are traveled by virtual features when camera motion is not confined to a plane. This generalization includes a number of mathematical results that substantially enhance the present understanding of specular surface geometry. An algorithm is developed that uniquely recovers 3D surface profiles using a single virtual feature tracked from the occluding boundary of the object. All theoretical derivations and proposed algorithms are substantiated by experiments.     

利用残差密集网络的运动模糊复原方法

针对图像生成过程中由于物体运动或相机抖动产生的运动模糊问题,提出了利用残差密集网络的运动模糊图像复原方法。设计对抗网络结构,以残差密集网络为生成器,通过长短连接实现不同层次特征的融合,生成复原图像,以深度卷积网络为判别器,判断图像真伪,在生成器和判别器的对抗中提高网络性能;采用对抗损失和内容损失结合的损失函数,提高网络的复原效果;以端到端的方式,省略模糊核的估计过程,输入模糊图像直接获取复原图像。实验结果表明,该方法能够取得较好的复原效果。     

基于相机姿势全局运动估计及改进修复算法的视频稳定技术

在视频稳定的过程中,由于摄像机的运动,造成图像的扭曲.针对这种情况,提出一种基于相机姿势的全局运动估计,同时为了克服图像拼接后,部分区域像素丢失的问题,使用改进后调和模型来修复缺少的像素.算法首先提取特征不变量,然后基于这些特征不变量去估计摄像机的运动矢量,相乘各帧间的运动矢量,可以得到每一帧参考第一帧的运动矢量.运用这个矢量可以很好地计算出没有扭曲的图像.运用计算出的图像与视频帧进行拼接,可以很好的解决图像的扭曲的问题.然而,图像拼接完成后可能导致部分区域像素缺少,为了填充缺少像素,算法使用了改进的调和模型来修复缺少区域.实验结果表明,基于相机姿势的全局运动估计可以很好的解决图像扭曲的问题,同时改进的调和模型可以高效的完成对图像的修复.     

一种鲁棒的基于两个特征点的深度估计方法

This paper presents a novel depth estimation method based on feature points. Two points are selected arbitrarily from an object and their distance in the space is assumed to be known.The proposed technique can estimate simultaneously their depths according to two images taken before and after a camera moves and the motion parameters of the camera may be unknown. In addition, this paper analyzes the ways to enhance the precision of the estimated depths and presents a feature point image coordinates search algorithm to increase the robustness of the proposed method.The search algorithm can find automatically more accurate image coordinates of the feature points based on their detected image coordinates. Experimental results demonstrate the efficiency of the presented method.     

Adaptive Visual Servoing Using Point and Line Features With an Uncalibrated Eye-in-Hand Camera

This paper presents a novel approach for image-based visual servoing of a robot manipulator with an eye-in-hand camera when the camera parameters are not calibrated and the 3-D coordinates of the features are not known. Both point and line features are considered. This paper extends the concept of depth-independent interaction (or image Jacobian) matrix, developed in earlier work for visual servoing using point features and fixed cameras, to the problem using eye-in-hand cameras and point and line features. By using the depth-independent interaction matrix, it is possible to linearly parameterize, by the unknown camera parameters and the unknown coordinates of the features, the closed-loop dynamics of the system. A new algorithm is developed to estimate unknown parameters online by combining the Slotine–Li method with the idea of structure from motion in computer vision. By minimizing the errors between the real and estimated projections of the feature on multiple images captured during motion of the robot, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values up to a scale. On the basis of the nonlinear robot dynamics, we proved asymptotic convergence of the image errors by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller.      

A two‐stage sampling for robust feature matching

For any visual feature‐based SLAM (simultaneous localization and mapping) solutions, to estimate the relative camera motion between two images, it is necessary to find “correct” correspondence between features extracted from those images. Given a set of feature correspondents, one can use a n‐point algorithm with robust estimation method, to produce the best estimate to the relative camera pose. The accuracy of a motion estimate is heavily dependent on the accuracy of the feature correspondence. Such a dependency is even more significant when features are extracted from the images of the scenes with drastic changes in viewpoints and illuminations and presence of occlusions. To make a feature matching robust to such challenging scenes, we propose a new feature matching method that incrementally chooses a five pairs of matched features for a full DoF (degree of freedom) camera motion estimation. In particular, at the first stage, we use our 2‐point algorithm to estimate a camera motion and, at the second stage, use this estimated motion to choose three more matched features. In addition, we use, instead of the epipolar constraint, a planar constraint for more accurate outlier rejection. With this set of five matching features, we estimate a full DoF camera motion with scale ambiguity. Through the experiments with three, real‐world data sets, our method demonstrates its effectiveness and robustness by successfully matching features (1) from the images of a night market where presence of frequent occlusions and varying illuminations, (2) from the images of a night market taken by a handheld camera and by the Google street view, and (3) from the images of a same location taken daytime and nighttime.     

适用于旋转摄像下目标检测的图像补偿

目的 摄像机旋转扫描条件下的动目标检测研究中,传统的线性模型无法解决摄像机旋转扫描运动带来的图像间非线性变换问题,导致图像补偿不准确,在动目标检测时将引起较大误差,造成动目标虚假检测。为解决这一问题,提出了一种面阵摄像机旋转扫描条件下的图像补偿方法,其特点是能够同时实现背景运动补偿和图像非线性变换补偿,从而实现动目标的快速可靠检测。方法 首先进行图像匹配,然后建立摄像机旋转扫描非线性模型,通过参数空间变换将其转化为线性求解问题,采用Hough变换实现该方程参数的快速鲁棒估计。解决摄像机旋转扫描条件下获取的图像间非线性变换问题,从而实现图像准确补偿。在此基础上,可以利用帧间差分等方法检测出运动目标。结果 实验结果表明,在摄像机旋转扫描条件下,本文方法能够同时实现图像间的背景运动补偿和非线性变换补偿,可以去除大部分由于立体视差效应（parallax effects）产生的匹配错误。并且在实验中,本文方法处理速度可以达到50帧/s,满足实时性要求。结论 在面阵摄像机旋转扫描的条件下,相比于传统的基于线性模型的图像补偿方法,本文方法能够快速、准确地在背景补偿的基础上同时解决图像间非线性变换问题,从而更好地提取出运动目标,具有一定的实用价值。     

基于DSP的运动目标跟踪系统

描述了一种以TMS320C6701数字信号处理器为核心的高速图像处理板和图像实时采集卡及摄像头构成的实时运动跟踪系统。在对采集的实时图像序列进行如十预处理后．采用了金字塔结构的图像存储方式和特征点跟踪算法埘运动目标进行跟踪．通过对特征点的运算得到目标运动的偏差怍为摄像头运动的参数,是后根据这些参数控制摄像云台持续跟踪运动目标的移动,最后还给出了在复杂背景下跟踪人体的实验结果。     

基于双目相机的视觉里程计

针对移动机器人视觉导航定位需求,提出一种基于双目相机的视觉里程计改进方案。对于特征信息冗余问题,改进ORB(oriented FAST and rotated BRIEF)算法,引入多阈值FAST图像分割思想,为使误匹配尽可能减少,主要运用快速最近邻和随机采样一致性算法;一般而言,运用的算法主要是立体匹配算法,此算法的特征主要指灰度,对此算法做出改进,运用一种新型的双目视差算法,此算法主要以描述子为特征,据此恢复特征点深度;为使所得位姿坐标具有相对较高的准确度,构造一种特定的最小二乘问题,使其提供初值,以相应的特征点三维坐标为基础,基于有效方式对相机运动进行估计。根据数据集的实验结果可知,所提双目视觉里程具有相对而言较好的精度及较高的实时性。     

基于Gaussian-Hermite矩的旋转运动模糊不变量

目的 模糊图像的分析与识别是图像分析与识别领域的重要方向。有些图像形成过程中成像系统与物体之间存在相对旋转运动,如因导弹高速自旋转造成的制导图像的旋转运动模糊。大多数对于这类图像的识别都需要先对模糊图像进行“去模糊”的预处理,且该类方法存在计算时间复杂度较高及不适定的问题。对此,提出一种直接提取旋转运动模糊图像中的不变特征,用于旋转运动模糊图像目标检索和识别。方法 本文以旋转运动模糊的退化模型为出发点,提出了旋转运动模糊Gaussian-Hermite (GH)矩,构造了一组由5个对旋转变换和旋转运动模糊保持不变性的GH矩不变量组成的特征向量(rotational motion blur Gaussian-Hermite moment invariants,RMB_GHMI-5),可从旋转变换和旋转运动模糊的图像中直接进行目标检索和识别,无需前置复杂的“去模糊”预处理过程。结果 在USC-SIPI (University of Southern California — Signal and Image Processing Institute)数据集上进行不变性实验,对原图进行不同程度的旋转变换叠加旋转运动模糊处理,证明RMB_GHMI-5对于旋转变换和旋转运动模糊具有良好的稳定性和不变性。在两个数据集上与同类4种方法进行图像检索实验比较,在80%召回率下,本文方法维数更少,相比性能第2的特征向量,在Flavia数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高25.89%、39.95%、22.79%和35.80%;在Butterfly Image数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高4.79、7.63%、5.65%和18.31%。同时,在上述8个测试数据集中进行对比实验以验证融合算法的有效性,结果表明本文提出的GH矩和几何矩相融合算法显著改善了图像检索效果。结论 本文提出的RMB_GHMI-5特征向量在旋转变换和旋转运动模糊下具有良好的不变性与稳定性,在图像检索抗噪性能方面表现优异。相比同类方法,本文方法更具实际应用价值。     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

简化UKF算法在摄像机标定中的应用

提出一种基于简化无迹卡尔曼滤波（UKF）算法的摄像机标定方法。将平面靶标图像上的不同特征点坐标视为同一个特征点在不同时刻的运动坐标。为避免欧拉角描述法带来的奇异问题,用单位四元数描述世界坐标系和摄像机坐标系之间的变换关系,选取摄像机内外参数作为系统状态变量。结合实际应用背景,简化标准UKF算法,将其用于摄像机参数估计,在保证标定精度的前提下降低运算复杂度。仿真结果表明了该方法的有效性。     

A probabilistic matching algorithm for computer vision

A model-based vision system attempts to find correspondences between features of an object model and features detected in an image for purposes of recognition, localization, or inspection. In this paper we pose the relational matching problem as a special case of the pattern complex recognition problem and propose a probabilistic model to describe the images of an object. This Bayesian approach allows us to make explicit statements of how an image is formed from a model, and hence define a natural matching cost that can be used to guide a heuristic search in finding the best observation mapping. Furthermore, we show that even though the nature of the feature matching problem is exponential, the use of the proposed algorithm keeps the size of the problem under control, by efficiently reducing the search space.