基于宏块的Hs光流算法运动估计优化研究

光流场属于一种运动参数,它不仅能够为人们提供目标物体的运动信息,还能使人们对运动物体进行有效的识别与定位,从而使人们更加有效的对目标物体进行运动估计,这也使光流场在计算机视觉领域中有着非常重要的应用.Hs光流算法对于提高光流场质量有着决定性的影响,但其在对目标物体运动信息进行识别、跟踪与估计时,常常存在计算量过大、易受噪声影响等问题,这也使Hs光流算法难以满足人们的数据处理需求.为此,有必要对Hs光流算法进行相应的改进,以此提高光流场质量.本文通过对Hs光流算法在运动估计优化中存在的问题及其相关影响因素进行分析,提出了Hs光流算法的改进思路,在此基础上结合宏块运动估计算法对改进后的Hs光流算法运动估计优化进行深入的研究.     

灰度图像最小误差阈值分割法的二维推广

一维最小误差阈值法假设了目标和背景的灰度分布服从混合正态分布. 考虑到噪声等因素对图像质量的影响, 本文在二维灰度直方图上, 基于二维混合正态分布假设, 给出一维最小误差阈值法的二维推广表达式. 为了提高算法的运行速度, 也给出了快速递推算法. 实验表明, 二维最小误差阈值法是一个有效的图像分割算法, 能够更好地适应目标和背景方差相差较大的图像及噪声图像的分割问题.     

热源运动的控制与仿真

为研究热源运动对热力系统性能的影响,需要给出热源在平面上的某一有限区域沿着运动轨迹进行移动时,控制热源运动状况的简化算法。物体的受热状态可用二维热传导方程描述,为求解这些方程,可用置换的方法进行简化,并利用分布式运动控制的算法予以实现。这些方法和算法都适用于二维对象,文章对热源平面移动时的运动控制算法进行了具体的设定和推导,并通过具体例子说明该算法的计算机实现过程与仿真方法。仿真结果表明,这些算法和方法是可行的、有效的。     

基于边界表示的体抽壳算法

形体的抽壳是构造具有薄壁外壳形状物体的一种有效方法.文章基于边界表示的正则形体,给出体抽壳运算的数学定义,并提出实现体抽壳运算的算法.该算法把形体的三维运算转化到曲面的参数域上,进行二维环之间的合并运算,从而降低了问题的复杂度.     

基于消失点的坐标校准算法

提出一种新的基于消失点的坐标校准算法,并将此算法应用于运动车辆的跟踪检测。该算法只需要知道消失点和已知尺寸物体在图像中的位置就可以实现坐标校准。与其他算法相比,该算法不需要预先知道摄像机的配置和参数,具有更广泛的适应性和更高的灵活性。在算法实现的过程中,使用了一维数组来表示前景分布矩阵,而非二维数组,从而有效地降低了算法空间复杂度。     

用Authorware设计MCAI课件中的仿真动画

针对目前用Authorware动画图标设计二维动画的不足，巧妙运用Authorware提供的变量和函数来设计物体平抛运动的仿真动画，并给出了物体做平抛运动动画设计的原理、算法和流程。     

二维物体变形技术现状与发展

二维物体变形技术在计算机动画、工业造型设计、科学计算可视化、电影特技等领域有着广泛的应用,具有十分重要的意义。近年来,有许多研究者提出了一些效果不错的算法,文中对这些算法进行了分析,对二维物体变形技术做了较全面的综述,探讨了现有二维物体变形技术中需要改进的关键问题,并给出了变形技术在未来的发展方向。     

二维物体变形技术现状与发展

二维物体变形技术在计算机动画、工业造型设计、科学计算可视化、电影特技等领域有着广泛的应用，具有十分重要的意义。近年来，有许多研究者提出了一些效果不错的算法，文中对这些算法进行了分析，对二维物体变形技术做了较全面的综述，探讨了现有二维物体变形技术中需要改进的关键问题，并给出了变形技术在未来的发展方向。     

碰撞环境下的动画编程技巧和应用

该文从动画编程效率和代码优化的角度,并结合CAI课件开发中涉及的动画实例,深入分析了基于二维向量算法和加权参数的物体受力分析算法在碰撞环境下的动画程序编写方面的技巧和应用,提高计算机模拟物体运动的效果。     

一种简单有效的运动目标检测算法

针对固定场景中运动目标检测遇到的运动目标状态突变,非运动目标干扰以及阴影等问题,提出了一种背景差分和帧间差分相结合的运动目标检测算法;该算法首先通过平均法背景模型确立背景,使用背景差分得到一幅二值化前景图像,然后将通过连续的多帧图像进行相邻帧差分得到的多幅二值化前景图像进行逻辑或运算,最后将运算结果同背景差分所得到的二值化前景图像进行逻辑与运算,得到最终运动目标区域;实验表明,该算法运算速度快,准确率高,并能满足实时检测的需要;不足之处是在摄像机与运动物体夹角很小的情况下,检测效果较差,但可以通过多台摄像机协同操作来达到理想的效果.     

Probabilistic models for robot-based object segmentation

This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot’s motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.     

Multiple motion scene reconstruction with uncalibrated cameras

In this paper, we describe a reconstruction method for multiple motion scenes, which are scenes containing multiple moving objects, from uncalibrated views. Assuming that the objects are moving with constant velocities, the method recovers the scene structure, the trajectories of the moving objects, the camera motion, and the camera intrinsic parameters (except skews) simultaneously. We focus on the case where the cameras have unknown and varying focal lengths while the other intrinsic parameters are known. The number of the moving objects is automatically detected without prior motion segmentation. The method is based on a unified geometrical representation of the static scene and the moving objects. It first performs a projective reconstruction using a bilinear factorization algorithm and, then, converts the projective solution to a Euclidean one by enforcing metric constraints. Experimental results on synthetic and real images are presented.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

Reconstruction of a Scene with Multiple Linearly Moving Objects

In this paper we describe an algorithm to recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously given a monocular image sequence. The number of the moving objects is automatically detected without prior motion segmentation. Assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. This representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. We also discuss solutions to the degenerate cases which can be automatically detected by the algorithm. Extension of the algorithm to weak perspective projections is presented as well. Experimental results on synthetic and real images show that the algorithm is reliable under noise.     

Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper, we introduce a modified version of the first multi-layer architecture. This version uses the basic parameters of the LIAC model to spatio-temporally build up to the desired extent the shapes of all moving objects present in a sequence of images. The influences of LIAC model parameters are explained in this paper, and we finally show some examples of the usefulness of the model proposed.     

基于马尔可夫随机场的运动分割研究

运动对象的分割技术一直是图像处理和计算机视觉领域的重要研究课题。采用一种将运动估计方法与马尔可夫随机场（MRF）模型相结合的运动分割方法。采用鲁棒统计技术与误差模型相结合构成运动估计的目标函数,运动模型为仿射运动,通过过松弛算法获得每种运动的运动参数;根据误差最小原则确定运动对应区域的初值,采用马尔可夫随机场（MRF）模型对运动估计结果进行平滑去噪。最后给出了该方法在通用图像实例上的实验结果。     

Motion estimation and segmentation

In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing.     

Rigid body segmentation and shape description from dense opticalflow under weak perspective

We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. Some previous attempts at segmentation via optical flow have focused on finding discontinuities in the flow field. While discontinuities do indicate a change in scene depth, they do not in general signal a boundary between two separate objects. The proposed method uses the fact that each independently moving object has a unique epipolar constraint associated with its motion. Thus motion discontinuities based on self-occlusion can be distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required. A Kalman filter based approach is used for tracking motion parameters with time     

视频图像序列运动参数估计与动态拼接

本文采用多重分层叠代算法来估计全局运动参数，并提出应用于动态拼接的运动分割新方法，实现既有摄像机运动又有物体运动的视频图像序列自动拼接。我们的方法基本步骤如下：首先进行全局运动参数的初始估计，并且在分层叠代过程中进行区域分类，得到初始运动模板。接着空间分割原始图像，先根据图像的空间属性由底向上分层合并图像空间区域，再利用视频图像时间属性进一步向上合并，得到图像空间分割结果。然后结合初始运动模板和图像空间分割结果，采用区域分类新方法重新对图像空间分割结果的每个区域进行分类。然后根据分类结果逐步精确求解全局运动参数。最后进行图像合成，得到全景拼接图像。我们的方法利用了多重分层叠代的优点，并且充分考虑到视频图像空间和时间上的属性，实现了运动物体和覆盖背景的精确分割，避免了遮挡问题对全局运动参数估计精度的影响。而且在图像合成时我们解决了拼接图可能产生模糊或某些区域不连续等问题。实验结果表明我们的方法实现了动态视频图像序列高质量的全景拼接。     

A fast and accurate moving object tracker in active camera model

Detecting and tracking moving objects within a scene is an essential step for high-level machine vision applications such as video content analysis. In this paper, we propose a fast and accurate method for tracking an object of interest in a dynamic environment (active camera model). First, we manually select the region of the object of interest and extract three statistical features, namely the mean, the variance and the range of intensity values of the feature points lying inside the selected region. Then, using the motion information of the background’s feature points and k-means clustering algorithm, we calculate camera motion transformation matrix. Based on this matrix, the previous frame is transformed to the current frame’s coordinate system to compensate the impact of camera motion. Afterwards, we detect the regions of moving objects within the scene using our introduced frame difference algorithm. Subsequently, utilizing DBSCAN clustering algorithm, we cluster the feature points of the extracted regions in order to find the distinct moving objects. Finally, we use the same statistical features (the mean, the variance and the range of intensity values) as a template to identify and track the moving object of interest among the detected moving objects. Our approach is simple and straightforward yet robust, accurate and time efficient. Experimental results on various videos show an acceptable performance of our tracker method compared to complex competitors.