基于并行隔帧差分光流场与灰度分析综合算法的运动目标检测

提出了隔帧差分的概念,并将其应用于光流场算法中,用改造后的隔帧差分光流场算法成功克服了传统光流场算法的不足(传统光流场算法不能识别帧间位移小于一个像元的运动目标).进一步对光流场计算结果与原始图像进行灰度综合分析,使目标的探测更加准确,减少了虚警和误判.采用不同间隔的序列差分构成并行识别系统结构,使整个系统能够探测各种不同速度的运动目标.实验结果表明:提出的运动目标检测算法能够准确有效地检测出不同速度的运动目标.     

动态背景下基于光流场分析的运动目标检测算法

针对现有动态背景下运动目标检测算法的不足,提出一种基于光流场分析的运动目标检测算法.首先根据前背景在光流梯度幅值和光流矢量方向上的差异确定目标的大致边界,然后通过点在多边形内部原理获得边界内部的稀疏像素点,最后以超像素为节点,利用混合高斯模型拟合的表观信息和超像素的时空邻域关系构建马尔可夫随机场模型的能量函数,并通过使目标函数能量最小化得到最终的运动目标检测结果.该算法不需要任何先验假设,能够同时处理动态背景和静态背景两种情况.多组实验结果表明,本文算法在检测的准确性和处理速度上均优于现有算法.     

Vehicle speed measurement based on gray constraint optical flow algorithm

Vehicle speed measurement (VSM) based on video images represents the development direction of speed measurement in the intelligent transportation systems (ITS). This paper presents a novel vehicle speed measurement method, which contains the improved three-frame difference algorithm and the proposed gray constraint optical flow algorithm. By the improved three-frame difference algorithm, the contour of moving vehicles can be detected exactly. Through the proposed gray constraint optical flow algorithm, the vehicle contour's optical flow value, which is the speed (pixels/s) of the vehicle in the image, can be computed accurately. Then, the velocity (km/h) of the vehicles is calculated by the optical flow value of the vehicle's contour and the corresponding ratio of the image pixels to the width of the road. The method can yield a better optical flow field by reducing the influence of changing lighting and shadow. Besides, it can reduce computation obviously, since it only calculates the moving target contour's optical flow value. Experimental comparisons between the method and other VSM methods show that the proposed approach has a satisfactory estimate of vehicle speed.     

复杂背景下运动点目标的检测算法

在复杂背景红外序列图像中,运动点目标的检测一直是研究的重点和难点。介绍了一种新的复杂背景下运动点目标的检测算法。首先根据点目标、背景干扰和噪声在红外图像中的差异,运用窗口大小不同的均值滤波器进行背景抑制以提高图像的信噪比,然后用一种门限法得到新的分割序列图像,最后采用改进后的隔帧差分光流场算法可有效地检测出点目标。仿真实验表明该算法优于传统光流场算法,能够检测帧间位移小于一个像元的运动目标,具有较好的检测性能,且实时性强。     

基于光流的运动小目标检测算法

运动目标的检测是目标识别与跟踪的关键技术之一。光流技术是一种以物体的运动特征来检测目标的方法,它的提出为运动小目标的检测开辟了新的空间。在一个搜索跟踪系统中使用光流技术检测和跟踪空中小目标,目标大概为5～10个像素,而且背景复杂,相机抖动,普通分割算法无法得到小目标。在目标的运动明显异于背景的情况下,通过利用基于光流的目标检测算法来检测出小目标,同时运用高斯金字塔模型,提高算法的运算速度。试验结果表明提出的基于光流的检测算法在背景运动的红外图像中取得了较好的效果。     

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution

In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.     

Optical authentication via photon-synthesized ghost imaging using optical nonlinear correlation

We present a method for optical authentication via photon-synthesized ghost imaging using optical nonlinear correlation. In ghost imaging, multiple series of photons recorded at the object beam arm can be arbitrarily controlled for the generation of synthesized objects. Ghost imaging with sparse reference intensity patterns provides a channel to effectively modulate the noise-like synthesized objects during the recovery, and the reconstructed (noise-like) objects, i.e., added or subtracted information, can be further authenticated by optical nonlinear correlation algorithm. It is expected that the proposed method can provide an effective and promising alternative for ghost-imaging-based optical processing.     

基于时空域的自动视频对象分割算法

提出一种在视频序列中从背景里分离出运动对象的方法.使用全局运动估计和补偿进行预处理后,对视频序列中相邻帧进行连续两次差分,利用自适应滤波滤除噪声,并进行形态学处理,差分交集技术消除覆盖、显露的背景及部分噪声,最后模板匹配和更新,不仅能够得到快速变化的对象,而且能够得到视频对象暂时停止运动的部分.实验结果表明,该方法能够自动从视频序列中较好地提取出运动对象,具有较强的鲁棒性.     

透过散射介质对直线运动目标的全光成像及追踪技术

光散射是限制光传输以及降低和破坏光学成像性能的主要因素,透过复杂散射介质对运动目标的全光成像是光学领域极具挑战性的技术之一.本文提出一种利用散斑差值自相关透过散射介质对运动目标进行实时追踪的方法.采用赝热光照明,基于光学记忆效应理论,通过对运动目标采集的两帧散斑做差值,然后做自相关运算,计算目标移动的距离,实现对目标的实时追踪,并且利用相位恢复算法进行简单处理就可以重建隐藏目标.对该方法进行了实验验证,成功地对隐藏的运动目标实现了成像与追踪.这种透过散射介质对运动目标的全光成像及实时追踪技术,在生物医学等领域具有重要应用潜力.     

Simulations of single and multiple swimmers with non-divergence free deforming geometries

We present a vortex particle method coupled with a penalization technique to simulate single and multiple swimmers in an incompressible, viscous flow in two and three dimensions. The proposed algorithm can handle arbitrarily deforming bodies and their corresponding non-divergence free deformation velocity fields. The method is validated on a number of benchmark problems with stationary and moving boundaries. Results include flows of tumbling objects and single and multiple self-propelled swimmers.     

基于混合自适应小波基的织物疵点检测算法

为了改进具有复杂纹理织物的疵点检测效果,提出了一种基于混合自适应小波基的织物疵点检测算法,采用各自优化的自适应小波基实现对不同层织物图像的分解变换。首先对正常图像和其经一层小波分解后的低频子图像优化得到混合自适应小波基,然后用该小波基将织物疵点图像进行二层小波分解,最后采用阈值法对径向子图像进行分割得到检测结果。实验结果证明,本文提出的算法能有效实现疵点检测,具有较好的疵点分割和定位结果。     

基于OpenCV的运动目标光流算法仿真

提出一种基于OpenCV图像库的运动目标处理算法仿真方法,介绍了OpenCV库的特点和VC6环境下的配置,通过调用库中的视频读写函数、图像特征计算和光流计算函数,得到运动目标的光流场,为运动目标状态分析和跟踪提供了基础,仅在图像库的基础上编写了少量代码就使算法得以实现,提高了编程效率。     

Fuzzy self-adaptive digital audio watermarking based on time-spread echo hiding

In this paper, a fuzzy self-adaptive digital audio watermarking method based on a time-spread echo hiding algorithm is proposed. Echo spreading is performed in the impulse response domain using PN sequences to achieve good robustness of echo hiding and high secrecy of spread spectrum. Fuzzy theory is employed in the embedding process to control the power of the watermark. Since fuzzy set theory is capable of performing complex nonlinear mappings between input and output spaces, it can effectively estimate the strength of a frame for each subband and ensure that the embedded watermark in the original audio is self-adaptive. A listening test and simulation results show that the algorithm is robust for common digital audio processing methods as attacks and that the quality of the audio is retained.     

一种改进的基于混合高斯模型的运动目标检测方法

混合高斯模型方法被广泛应用于运动目标检测,但是现有的混合高斯模型方法在应对噪声和光照突变时效果不佳,其运动目标检测的效果会受到严重影响。为了解决上述问题,提出了一种结合边缘混合高斯模型方法以及改进的基于邻域差方法的综合方法。该方法充分利用了边缘图像对于噪声和光照突变不敏感,以及邻域差方法可以去除部分噪声的特点,对图像序列进行综合处理。实验结果证明,该方法可以提升运动目标的检测率,降低误警率,可以更有效地应对噪声和光照突变的干扰,从而具有更准确的目标检测效果。     

基于感兴趣区域的幅型比变换新方法

基于人类视觉系统对运动目标比较敏感的特性,提出了一种新的幅型比变换方法.综合时空信息提取视频序列中的运动目标,利用图像修复技术对被提取运动目标后的视频图像进行背景修补.采用插值算法以不同的方式分别缩放运动目标和修复后的背景,并将变换后的运动目标与背景相叠加合成相应幅型比的视频图像.实验结果表明：此方法避免了已有幅型比变换方法的缺陷,保证了变换后运动目标的无失真性,与人类的视觉感知有较好的一致性.     

烟气组分浓度实时连续监测系统

采用紫外差分吸收光谱法对工业生产所排放的烟气（SO2、NOx等）浓度进行实时连续监测,为使现场光路调整更加方便,设计了单边插入式四自由度调整探头,有效地提高光耦合效率。研究了光谱自适应算法,根据所接收到的光谱强度,自动调整光谱仪积分时间,延长了光源的使用寿命。将所研制的系统进行了现场应用实验,SO2、NO的测量误差小于±2％。     

融合RANSAC光流跟踪法和特征点匹配法的视觉里程计

为了解决光流跟踪法定位精度不足、误差累积和特征点匹配法耗时久的问题,设计了一种将随机抽样一致性（random sample consensus,RANSAC）光流跟踪法和改进的特征点匹配法结合的视觉里程计。利用RANSAC光流跟踪法对关键帧之间的小规模运动进行估计,RANSAC算法对光流跟踪的误匹配点进行剔除,大大降低了光流跟踪法存在的误匹配;而关键帧之间的运动估计则利用改进的特征点匹配法,以修正光流跟踪法的估计误差;最后利用卡尔曼滤波将RANSAC光流跟踪法和改进的特征点匹配法进行融合。实验结果表明:该文的算法能够克服光流跟踪法精度不足、误差累积的问题,将平均相对误差由15.5%提升到了2.6%;同时也能在一定程度上提高特征点匹配法的速度,将特征点匹配法的平均耗费时间由37.28 ms提升到了21.07 ms。     

光谱特征分析的城市道路提取

道路是城市中典型的人造地物。利用高分辨率影像进行城市道路提取,对城市规划、交通发展具有重要意义。由于地物光谱的混淆性和异质性,利用传统基于光谱的分类方法很难将道路与其他城市地物区分开。针对这一问题,提出了一种利用道路边缘结构信息进行分类的方法,边缘作为光谱衍生信息对线性地物(如,道路等)识别具有明显的意义。首先,根据全色光谱波段纹理信息,利用改进的自适应Mean Shift算法进行边缘检测,最大限度减少噪声与伪边缘;然后,对边缘图像中的线段进行编组,利用统计模型依次对边缘线段求取统计特征,并将该统计特征与多光谱特征结合作为总分类特征;最后,利用监督学习方法对城市道路样本进行学习并对整个实验区域进行分类。结果表明将光谱信息与边缘统计特征融合对道路的识别精度为93%,相比传统方法78%的精度有显著的提高,因此,该方法是一种有效、可行的高分辨率遥感图像城市道路提取方法。     

Spatio-temporal segmentation of moving objects using edge features in infrared videos

In this paper, a novel method is proposed for spatio-temporal segmentation of moving objects using edge features in infrared videos. We define motion saliency of edge (MSoE) to generate the MSoE-map. The seeds of moving objects are extracted from the MSoE-map by using Otsu's method and subsequently compensated by historical data. An improved layer-based region growing method is applied to the seeds to achieve spatial segmentation of moving objects. The region growing method has an adjustable growing threshold. So, one of the focuses of our work is how to determine the best growing threshold. A Markov Random Field (MRF) based criterion with maximum a posterior (MAP) estimation principle is proposed for performance evaluation of moving object segmentation without ground truth (GT) in infrared videos. This criterion can be considered as an object function of threshold determination during global searching. The global optimum is accomplished by using simulated annealing (SA) algorithm to obtain the best growing threshold. The final segmentation mask of moving objects is grown from the seeds with the best growing threshold. Experimental results are provided to illustrate that the proposed method has better performance for moving object segmentation with fewer effects of object-background misclassification in infrared videos.     

基于特征点光流聚类的复杂背景中运动车辆检测

为了准确、快速的在动态场景中对运动车辆进行检测,提出一种基于特征点光流聚类的车辆检测方法。该方法取Harris角点为特征量,通过对特征点做光流提取来剔除一些没有运动的干扰角点,然后再通过模糊U邻域(FUNN)聚类算法剔除噪音、孤立点和不感兴趣样本并实现前景和背景的分离,最后通过设定阈值判断前景目标是否是车辆。实验结果证明在复杂的动态场景中该算法具有更高的车辆识别率。