Curvelet-Based Snake for Multiscale Detection and Tracking of Geophysical Fluids

Detection and target tracking have an application to many scientific problems. The approach developed in this paper is motivated by the applications of detection and tracking characteristic deformable structures in geophysical fluids. We develop an integrated detection and tracking method of geophysical fluids based on a discrete curvelet representation of the information characterizing the targets. Curvelets are in some sense geometric wavelets, allowing an optimal sparse representation of two-dimensional piecewise continuous objects with C ²-singularities. The proposed approach first identifies a consistent vortex by a curvelet-based gradient-vector-flow snake and then establishes the motion correspondence of the snaxels between successive time frames by a constructed so-called semi-T or comp-T multiscale motion-estimation method based on the geometric wavelets. Furthermore, a combination of total-variation regularization and cycle-spinning techniques effectively removes false matches and improves significantly the estimation. Numerical experiments at each stage demonstrate the performance of the proposed tracking methodology for temporal oceanographic satellite image sequences corrupted by noise, with weak edges and submitted to large deformations, in comparison to conventional methods     

基于光流法的多分辨率电子稳像算法

针对动载体摄像系统中视频序列受载体姿态运动及抖动的干扰而出现的不稳定现象,提出一种基于光流算法的多分辨率电子稳像算法。首先,通过划定有效的运动估算区域取代对整帧图像的计算以降低计算量;然后,利用基于光流算法的多分辨率分层运动估计快速并精确地计算出包含平移、旋转以及缩放运动的相邻帧间仿射变换参数;最后,采用固定帧补偿算法,利用求得的仿射变换参数,对图像进行运动补偿,消除或减轻图像序列帧间的随机抖动,达到稳像的目的。实验结果表明,针对包含运动目标的动态场景,该算法可以精确地检测出视频序列帧间平移、旋转以及缩放等复杂的抖动,水平和垂直方向的稳像精确度小于1pixel,保证视频序列的稳定输出,可应用于目标跟踪系统中。     

噪声背景中点目标检测的多比例法

要把淹没在噪声背景中的运动点目标检测出来是一个十分困难的问题.本文提出了一个基于多比例几何技术的运动目标检测新方法.以高斯函数作为滤波核,用多比例方法构造了一个四维比例时空空间.利用一阶几何特征导出了目标的运动约束方程.基于目标图像的速度一致性和像素连接性,研究了解决运动检测与速度估计的方法. 描述了速度平面上约束直线聚类点或聚类区的粗精搜索算法.给出了精确地搜索最优比例的精度度量.提出的实验和比较结果,支持了这些研究.     

地基红外云图云导风方法研究进展

利用连续观测的云序列图像进行风场探测的技术已经较为成熟,目前主要应用于卫星云导风中,可以获取较大范围、大尺度的区域风场信息,但利用地基观测云图进行云导风方面的研究还较少。分析了地基测云仪器的发展现状,提出了采用高时空分辨率的地基红外云图,通过阈值确定云高,利用目标匹配方法、目标追踪方法进行示踪云的选择,利用快速搜索算法进行示踪云的搜索,以及地基云导风计算的方法,并分析了导风结果的质量控制思路。利用该类地基红外云图导风资料可以与常规高空风探测手段、卫星云导风资料互为补充。     

基于目标变换模型的跟踪方法研究

图像目标跟踪是计算机视觉领域中富有挑战性的工作之一,但已有的算法大多都存在一定的局限性。针对目标相关匹配方法难以处理图像序列中目标所具有的连续性的尺度变化、旋转、变形等问题,通过在相邻两帧图像之间建立目标相对变化关系的数学模型,并依据该变换关系的数学描述及一定的相关测度对跟踪问题进行最优化建模,将目标跟踪问题转化为目标变换模型参数的最优化求解问题,最后利用L-M算法对上述优化问题进行求解,实现目标跟踪。实验结果表明,该方法对发生连续性平移、尺度、旋转、变形等变化的目标具有良好的跟踪精度,且对图像质量要求不高。     

Detection of aberrant behaviour in home environments from video sequence

We introduce an application for the detection of aberrant behaviour within home based environments, with a focus on repetitive actions, which may be present in instance of persons suffering from dementia. Video based analysis has been used to detect the motion of a person within a given scene in addition to tracking them over the time. Detection of repetitive actions has been based on the analysis of a person’s trajectory using the principles of signal correlation. Along with the ability to detect repetitive motion the developed approach also has the ability to measure the amount of activity/inactivity within the scene during a given period of time. Our results showed that the developed approach had the ability to detect all patterns in the data set examined with an average accuracy of 96.67%. This work has therefore validated the proposed concept of video based analysis for the detection of repetitive activities.     

Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.     

Fusion of physically-based registration and deformation modelingfor nonrigid motion analysis

In our previous work, we used finite element models to determine nonrigid motion parameters and recover unknown local properties of objects given correspondence data recovered with snakes or other tracking models. In this paper, we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea of our approach is that a finite element (FEM) model incorporating material properties of the object can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdorff distance to recover correspondences. The method is evaluated with respect to speed and accuracy. Noise sensitivity issues are addressed. Advantages of the proposed approach are demonstrated using man-made elastic materials and human skin motion. Experiments with regular grid features are used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown, however, that the new method does not require a sampling/correspondence template and can adapt the model to available object features. Usefulness of the method is presented not only in the context of tracking and motion analysis, but also for a burn scar detection application.     

Space-time image sequence analysis: object tunnels and occlusion volumes.

We address the issue of image sequence analysis jointly in space and time. While typical approaches to such an analysis consider two image frames at a time, we propose to perform this analysis jointly over multiple frames. We concentrate on spatiotemporal segmentation of image sequences and on analysis of occlusion effects therein. The segmentation process is three-dimensional (3-D); we search for a volume carved out by each moving object in the image sequence domain, or "object tunnel," a new space-time concept. We pose the problem in variational framework by using only motion information (no intensity edges). The resulting formulation can be viewed as volume competition, a 3-D generalization of region competition. We parameterize the unknown surface to be estimated, but rather than using an active-surface approach, we embed it into a higher dimensional function and apply the level-set methodology. We first develop simple models for the detection of moving objects over static background; no motion models are needed. Then, in order to improve segmentation accuracy, we incorporate motion models for objects and background. We further extend the method by including explicit models for occluded and newly exposed areas that lead to "occlusion volumes," another new space-time concept. Since, in this case, multiple volumes are sought, we apply a multiphase variant of the level-set method. We present various experimental results for synthetic and natural image sequences.     

基于红外压缩成像的点目标跟踪方法研究

目前压缩测量的应用研究主要集中在重构图像方面,但是很多应用中最终目的是检测和跟踪。直接基于压缩测量的检测和跟踪问题尚未解决。该文首次建立一种压缩域到空间域的映射模型,并提出一种无需重构任何图像且直接从低维压缩测量中经解码进行目标跟踪的方法,并分析其应用于天基红外探测的可能性。该方法利用Hadamard测量矩阵构建红外压缩成像系统,采用自适应压缩背景差分法从低维压缩测量信息中分离背景和前景,再从压缩前景信息中解码目标空间位置,并结合数据关联和Kalman滤波算法解决了杂波环境下点目标跟踪问题。理论分析和仿真实验结果表明,该方法能利用少量压缩测量实现目标跟踪任务,并减小探测器规格及相关算法的计算复杂度和存储代价。     

多层时空切片联合的快速人体跟踪算法

针对传统人体跟踪方法中目标模型复杂、计算量大等问题,该文提出一种无目标模型的多层时空切片联合的人体跟踪算法。用多层时空切片中的多个动态区域表示人体,区域的选择无需使用任何预定义的目标区域模型。使用时空切片方法在图像序列空间中提取多层水平时空切片图像,在每层时空切片图像中,检测和跟踪潜在的运动区域,并根据区域运动一致性和空间一致性关系,将多个区域关联成不同的人体目标,实现多个人体目标的跟踪,从而将XYT 3维空间中的人体跟踪问题转化为多个XT 2维空间的区域联合跟踪问题。实验表明,该算法降低了跟踪的轨迹误差,满足实时性跟踪要求,同时通过多区域的联合增强了跟踪算法的抗干扰能力,即在人体部分区域丢失的情况下仍能有效跟踪。     

Correspondence processes in dynamic scene analysis

One of the fundamental problems in dynamic scene analysis is the tracking of objects from frame to frame. A general approach to tracking is to establish correspondences between points, or sets of points, between frames and then group the sets into objects based upon similarity of motion. This paper will focus on processes for establishing the correspondence between sets of points in successive frames. A succession of correspondence processes are discussed, based on the factors which contribute to the complexity of the correspondence problem.     

确定三维运动参数的张量分析方法

利用中心投影图象序列估计三维运动参数,需要知道空间运动物体的特征与图象平面上投影坐标之间的对应关系。为了避免利用特征对应关系,本文给出在仿射变换系统中的张量分析方法,也给出模拟实验结果数据。     

A TENSOR ANALYSIS METHOD FOR ESTIMATING 3-D MOTION PARAMETERS

By using the center projection image sequence to estimate 3-D motion parameters,one needs to know the corresponding relationship between the feature of motion object in spaceand the projection coordinate on image plane.In order to avoid using the relationship of featurecorrespondence,the tensor analysis method in the affine transformation system is presented,andthe simulation data of experimental results are given.     

基于奇异值分解的特征跟踪方法

在传统的基于模板匹配的跟踪方法中，均是给定一个模板，然后从图像中各个位置取出一个个与模板大小一致的区域进行相似性度量，找出与模板距离最小的一个区域作为当前模板，以便进行下一步的匹配跟踪工作。在景象匹配和相关跟踪过程中，由于所面临的大多数是变化的场景，实时获取的图像与预存模板之间存在比较大的差异，传统相关匹配方法的应用就会受到限制；而且在跟踪过程中，随时更新模板会造成跟踪性能对扰动过分敏感，从而产生漂移。首先拍摄目标不同角度的图像（尽可能包含目标可能出现的所有情况），构成目标图像训练集合，抽取出特征矩阵，对它进行奇异值分解，构成一个关于目标的多维空间。然后再用匹配方法在全局范围搜索，找出目标的大致位置，并利用收敛方法在确定的大致位置内进行搜索，确定目标的仿射变换系数，从而得到一个目标位置的确切描述。     

Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences

In this paper, we address the problem of estimating 3-D motions of a stratified atmosphere from satellite image sequences. The analysis of 3-D atmospheric fluid flows associated with incomplete observation of atmospheric layers due to the sparsity of cloud systems is very difficult. This makes the estimation of dense atmospheric motion field from satellite image sequences very difficult. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into cloud layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of 3-D wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete 3-D space, using a multilayer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences.      

Efficient optical camera tracking in virtual sets

Optical tracking systems have become particularly popular in virtual studios applications tending to substitute electromechanical ones. However, optical systems are reported to be inferior in terms of accuracy in camera motion estimation. Moreover, marker-based approaches often cause problems in image/video compositing and impose undesirable constraints on camera movement, present work introduces a novel methodology for the construction of a two-tone blue screen, which allows the localization of camera in three-dimensional (3-D) space on the basis of the captured sequence. At the same time, a novel algorithm is presented for the extraction of camera's 3-D motion parameters based on 3-D-to-two-dimensional (2-D) line correspondences. Simulated experiments have been included to illustrate the performance of the proposed system.     

Light bottle transformer based large scale point cloud classification

With the rapid development of computer vision, point clouds technique was widely used in practical applications, such as obstacle detection, roadside detection, smart city construction, etc. However, how to efficiently identify the large scale point clouds is still an open challenge. For relieving the large computation consumption and low accuracy problem in point cloud classification, a large scale point cloud classification framework based on light bottle transformer (light-BotNet) is proposed. Firstly, the two-dimensional (2D) and three-dimensional (3D) feature values of large scale point cloud were extracted for constructing point cloud feature images, which employed the prior knowledge to normalize the point cloud features. Then, the feature images are input to the classification network, and the light-BotNet network is applied for point cloud classification. It is an interesting attempt to combine the traditional image features with the transformer network. For proving the performance of the proposed method, the large scale point cloud benchmark Oakland 3D is utilized. In the experiments, the proposed method achieved 98.1% accuracy on the Oakland 3D dataset. Compared with the other methods, it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.     

复杂背景下红外运动点目标检测算法研究

讨论了复杂背景下低信噪比运动点目标的检测和跟踪问题，提出了基于卡尔曼滤波理论的背景预测，数学形态学膨胀累加，图像流航迹关联和二级并行假设检验的点目标检测方法。实验结果表明该算法能够较大程度提高红外图像的信噪比，有效地检测和跟踪点目标，并且能够解决目标丢失以及跟踪过程中出现目标的问题。     

Analysis and compensation of rolling shutter effect

Due to the sequential-readout structure of complementary metal-oxide semiconductor image sensor array, each scanline of the acquired image is exposed at a different time, resulting in the so-called electronic rolling shutter that induces geometric image distortion when the object or the video camera moves during image capture. In this paper, we propose an image processing technique using a planar motion model to address the problem. Unlike previous methods that involve complex 3-D feature correspondences, a simple approach to the analysis of inter- and intraframe distortions is presented. The high-resolution velocity estimates used for restoring the image are obtained by global motion estimation, BEzier curve fitting, and local motion estimation without resort to correspondence identification. Experimental results demonstrate the effectiveness of the algorithm.